ZOMATO_RESTAURANT_ANALYSIS_ML(5)_Project.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "private_outputs": true,
      "provenance": [],
      "collapsed_sections": [
        "mDgbUHAGgjLW",
        "fge-S5ZAYoAp",
        "RoGjAbkUYoAp",
        "F6T5p64dYrdO",
        "y-Ehk30pYrdP",
        "bamQiAODYuh1",
        "QHF8YVU7Yuh3",
        "qYpmQ266Yuh3",
        "bbFf2-_FphqN",
        "4_0_7-oCpUZd",
        "hwyV_J3ipUZe",
        "3yB-zSqbpUZe",
        "dEUvejAfpUZe",
        "bn_IUdTipZyH",
        "49K5P_iCpZyH",
        "OB4l2ZhMeS1U",
        "ArJBuiUVfxKd",
        "4qY1EAkEfxKe",
        "PiV4Ypx8fxKe",
        "TfvqoZmBfxKf",
        "dJ2tPlVmpsJ0",
        "JWYfwnehpsJ1",
        "-jK_YjpMpsJ2",
        "HAih1iBOpsJ2",
        "zVGeBEFhpsJ2",
        "bmKjuQ-FpsJ3",
        "Fze-IPXLpx6K",
        "7AN1z2sKpx6M",
        "9PIHJqyupx6M",
        "_-qAgymDpx6N",
        "Z-hykwinpx6N",
        "h_CCil-SKHpo",
        "cBFFvTBNJzUa",
        "-Kee-DAl2viO",
        "gIfDvo9L0UH2"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/codewithalishakhan/Zomato_Restaurant_Analysis/blob/main/ZOMATO_RESTAURANT_ANALYSIS_ML(5)_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Name**    -  Zomato Restaurant Clustering And Sentiment Analysis\n",
        "\n"
      ],
      "metadata": {
        "id": "vncDsAP0Gaoa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Project Type**    - Unsupervised ML\n",
        "##### **Contribution**    - Individual\n",
        "Name - **Alisha Khan**\n",
        "\n",
        "Cohort - **Nairobi**\n",
        "\n"
      ],
      "metadata": {
        "id": "beRrZCGUAJYm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "  \n",
        "\n",
        "\n",
        "![Screenshot_9.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "SjCBEt6kJpD7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Summary -**"
      ],
      "metadata": {
        "id": "FJNUwmbgGyua"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Zomato is an Indian restaurant aggregator and food delivery start-up founded by **Deepinder Goyal** and **Pankaj Chaddah** in 2008.\n",
        "This project analyzes Zomato's restaurant data for customer sentiments and company growth. Through sentiment analysis and restaurant segmentation, it derives actionable insights and addresses key business cases. Utilizing customer reviews and metadata, the project aims to enhance the dining experience and identify areas for improvement. Visualizations aid in understanding customer preferences and clustering restaurants for strategic decision-making. Ultimately, the project facilitates both customer satisfaction and company development within the diverse Indian food industry.\n"
      ],
      "metadata": {
        "id": "F6v_1wHtG2nS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **GitHub Link -**"
      ],
      "metadata": {
        "id": "w6K7xa23Elo4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://github.com/codewithalishakhan/Zomato_Restaurant_Analysis\n",
        "\n"
      ],
      "metadata": {
        "id": "h1o69JH3Eqqn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Problem Statement**\n"
      ],
      "metadata": {
        "id": "yQaldy8SH6Dl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The challenge is to effectively analyze Zomato's restaurant data to understand customer sentiments, segment restaurants, and address business cases. This entails deriving actionable insights from reviews, clustering restaurants for strategic decisions, and enhancing overall service quality. The goal is to facilitate informed decision-making for consumers and faster growth opportunities for businesses in the dynamic Indian food industry landscape.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DpeJGUA3kjGy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **General Guidelines** : -  "
      ],
      "metadata": {
        "id": "mDgbUHAGgjLW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Well-structured, formatted, and commented code is required.\n",
        "2.   Exception Handling, Production Grade Code & Deployment Ready Code will be a plus. Those students will be awarded some additional credits.\n",
        "     \n",
        "     The additional credits will have advantages over other students during Star Student selection.\n",
        "       \n",
        "             [ Note: - Deployment Ready Code is defined as, the whole .ipynb notebook should be executable in one go\n",
        "                       without a single error logged. ]\n",
        "\n",
        "3.   Each and every logic should have proper comments.\n",
        "4. You may add as many number of charts you want. Make Sure for each and every chart the following format should be answered.\n",
        "        \n",
        "\n",
        "```\n",
        "# Chart visualization code\n",
        "```\n",
        "            \n",
        "\n",
        "*   Why did you pick the specific chart?\n",
        "*   What is/are the insight(s) found from the chart?\n",
        "* Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason.\n",
        "\n",
        "5. You have to create at least 15 logical & meaningful charts having important insights.\n",
        "\n",
        "\n",
        "[ Hints : - Do the Vizualization in  a structured way while following \"UBM\" Rule.\n",
        "\n",
        "U - Univariate Analysis,\n",
        "\n",
        "B - Bivariate Analysis (Numerical - Categorical, Numerical - Numerical, Categorical - Categorical)\n",
        "\n",
        "M - Multivariate Analysis\n",
        " ]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "6. You may add more ml algorithms for model creation. Make sure for each and every algorithm, the following format should be answered.\n",
        "\n",
        "\n",
        "*   Explain the ML Model used and it's performance using Evaluation metric Score Chart.\n",
        "\n",
        "\n",
        "*   Cross- Validation & Hyperparameter Tuning\n",
        "\n",
        "*   Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart.\n",
        "\n",
        "*   Explain each evaluation metric's indication towards business and the business impact pf the ML model used.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ZrxVaUj-hHfC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***Let's Begin !***"
      ],
      "metadata": {
        "id": "O_i_v8NEhb9l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***1. Know Your Data***"
      ],
      "metadata": {
        "id": "HhfV-JJviCcP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import Libraries"
      ],
      "metadata": {
        "id": "Y3lxredqlCYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from sklearn.cluster import KMeans\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "pd.set_option('display.max_columns',None)\n",
        "\n",
        "\n",
        "import warnings\n",
        "warnings .filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "M8Vqi-pPk-HR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Loading"
      ],
      "metadata": {
        "id": "3RnN4peoiCZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Dataset\n",
        "# from google.colab import drive\n",
        "# drive .mount ('/content/drive')\n",
        "\n",
        "meta_df_1 = pd.read_csv('/content/Zomato Restaurant names and Metadata.csv')\n",
        "meta_df_1\n"
      ],
      "metadata": {
        "id": "4CkvbW_SlZ_R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2 = pd.read_csv(\"/content/Zomato Restaurant reviews.csv\")\n",
        "review_df_2"
      ],
      "metadata": {
        "id": "shyhARF0BuuA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset First View"
      ],
      "metadata": {
        "id": "x71ZqKXriCWQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset First Look\n",
        "meta_df_1.head()"
      ],
      "metadata": {
        "id": "LWNFOSvLl09H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.head()"
      ],
      "metadata": {
        "id": "MsdY5F0rPI9k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "meta_df_1.tail()"
      ],
      "metadata": {
        "id": "-79IAkchPoqH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.tail()"
      ],
      "metadata": {
        "id": "nOyT825tPsCt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Rows & Columns count"
      ],
      "metadata": {
        "id": "7hBIi_osiCS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Rows & Columns count\n",
        "meta_df_1.shape"
      ],
      "metadata": {
        "id": "Kllu7SJgmLij"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.shape"
      ],
      "metadata": {
        "id": "kjI87rkRQpzF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Information"
      ],
      "metadata": {
        "id": "JlHwYmJAmNHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Info\n",
        "meta_df_1.info()"
      ],
      "metadata": {
        "id": "e9hRXRi6meOf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.info()"
      ],
      "metadata": {
        "id": "jqOjsSDIQzMk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Duplicate Values"
      ],
      "metadata": {
        "id": "35m5QtbWiB9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Duplicate Value Count\n",
        "meta_df_1.duplicated().sum()"
      ],
      "metadata": {
        "id": "1sLdpKYkmox0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.duplicated().sum()"
      ],
      "metadata": {
        "id": "tiNvu5_PRPQJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Missing Values/Null Values"
      ],
      "metadata": {
        "id": "PoPl-ycgm1ru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values/Null Values Count\n",
        "meta_df_1.isna().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "GgHWkxvamxVg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.isna().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "_g4-RvKAUU6k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the missing values\n",
        "plt.figure(figsize=(10,5))\n",
        "sns.heatmap(meta_df_1.isnull(),cmap='plasma',annot=False,yticklabels=False)\n",
        "plt.title(\"Visualizing missing values\");"
      ],
      "metadata": {
        "id": "3q5wnI3om9sJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What did you know about your dataset?"
      ],
      "metadata": {
        "id": "H0kj-8xxnORC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are two data sets provided for the analysis. One is based on the information about the various restaurant that uses the Zomato delivery services ,the file named as meta data. Another one is based on reviews given by the customers to those particular restaurants that are mentioned the meta data .Meta files has the dimension 105 rows and 6 columns as variables and the reviews files has the dimensions of 1000 rows and 7 columns . Also there are 36 duplicates values in the review file but there are no duplicates in meta data file. In addition to that there are also missing values in both the datasets."
      ],
      "metadata": {
        "id": "gfoNAAC-nUe_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***2. Understanding Your Variables***"
      ],
      "metadata": {
        "id": "nA9Y7ga8ng1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Columns\n",
        "meta_df_1.columns\n"
      ],
      "metadata": {
        "id": "j7xfkqrt5Ag5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.columns"
      ],
      "metadata": {
        "id": "Uy2K6EWfCnEf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Describe\n",
        "meta_df_1.describe()"
      ],
      "metadata": {
        "id": "DnOaZdaE5Q5t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.describe()"
      ],
      "metadata": {
        "id": "Ew5BlKavCw1G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Variables Description"
      ],
      "metadata": {
        "id": "PBTbrJXOngz2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Zomato Restaurant ans Metadata Use this dataset for clustering part.\n",
        "\n",
        "Name: Name of the restaurants.\n",
        "\n",
        "Links: URL links of the restaurants.\n",
        "\n",
        "Cost: Per person estimated Cost of dining.\n",
        "\n",
        "Collection: Tagging of restaurants w.r.t  Zomato categories.\n",
        "\n",
        "Cuisiness: Cuisiness served by the restaurants.\n",
        "\n",
        "Timings: Restaurant timing.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# Zomato Restaurants Reviews.\n",
        "\n",
        "Restaurant: Name of the restaurants.\n",
        "\n",
        "Reviewer: Name of the reviewers.\n",
        "\n",
        "Review: Review text.\n",
        "\n",
        "MetaData: Reviewer metadata- No. of Reviews and followers.\n",
        "\n",
        "Time: Date and Time of the review.\n",
        "\n",
        "Pictures: No.of pictures posted by review."
      ],
      "metadata": {
        "id": "aJV4KIxSnxay"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check Unique Values for each variable."
      ],
      "metadata": {
        "id": "u3PMJOP6ngxN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Unique Values for each variable.\n",
        "for items in meta_df_1 . columns. tolist():\n",
        "  print('The number of unique values in column' , items,'is', meta_df_1[items].nunique())"
      ],
      "metadata": {
        "id": "zms12Yq5n-jE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for items in review_df_2.columns.tolist():\n",
        "  print('The number of unique values in column' ,items , 'is' , review_df_2[items].nunique())"
      ],
      "metadata": {
        "id": "z35wJdwXGymr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. ***Data Wrangling***"
      ],
      "metadata": {
        "id": "dauF4eBmngu3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Wrangling Code"
      ],
      "metadata": {
        "id": "bKJF3rekwFvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_outlier(df,column):\n",
        "  plt.figure(figsize=(15,5))\n",
        "  plt.subplot(1,2,1)\n",
        "  plt.title('Before removing outliers')\n",
        "  sns.boxplot(df[column])\n",
        "  plt.subplot(1,2,2)\n",
        "  sns.distplot(df[column])\n",
        "  df = df[((df[column] - df[column].mean()) / df[column] .std().__abs__() < 3 )]\n",
        "  df = df[((df[column] - df[column].mean()) / df[column] .std().__abs__() > -3 )]\n",
        "\n",
        "  plt.figure(figsize=(15,5))\n",
        "  plt.subplot(1,2,1)\n",
        "  plt.title('After removing outliers')\n",
        "  sns.boxplot(df[column])\n",
        "  plt.subplot(1,2,2)\n",
        "  sns.distplot(df[column])\n",
        "\n"
      ],
      "metadata": {
        "id": "wk-9a2fpoLcV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# handling the missing values in meta data\n",
        "\n",
        "\n",
        "meta_df_1['Cost'] = meta_df_1['Cost'].astype(str)                                                          # Converting  'Cost' column to string type\n",
        "meta_df_1['Cost'] = meta_df_1['Cost'].str.replace(',', '').astype(float)\n",
        "meta_df_1.drop(['Links', 'Timings'],axis = 1 )\n",
        "meta_df_1.rename(columns = {'Name' : 'Restaurant'})\n",
        "meta_df_1['Collections'].fillna('Unknown')\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "VGf-q6NC2yb-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "remove_outlier(meta_df_1,'Cost')"
      ],
      "metadata": {
        "id": "p-2jtwLX6UAD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#merging both data frame\n",
        "resturent_df = meta_df_1.rename(columns = {'Name':'Restaurant'})\n",
        "merged_df = resturent_df.merge(review_df_2, on = 'Restaurant')\n",
        "merged_df.shape"
      ],
      "metadata": {
        "id": "rKQ41Ra01lio"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.head()\n",
        ""
      ],
      "metadata": {
        "id": "SyzlXbKV1v3q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What all manipulations have you done and insights you found?"
      ],
      "metadata": {
        "id": "MSa1f5Uengrz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Removing the Outliers.\n",
        "2. Drop the unneccessary columns as Links and Timings.\n",
        "3. Renamed the Column Name in Meta File as Restaurant.\n",
        "4. Filled the missing values in Collections column as 'Unknown'."
      ],
      "metadata": {
        "id": "LbyXE7I1olp8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***4. Data Vizualization, Storytelling & Experimenting with charts : Understand the relationships between variables***"
      ],
      "metadata": {
        "id": "GF8Ens_Soomf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA on MetaData file"
      ],
      "metadata": {
        "id": "KS4FFsTFROTq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "meta_df_1.head(5)"
      ],
      "metadata": {
        "id": "yaorotPCSbsZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Top_10_costly_restaurant = meta_df_1[['Name', 'Cost']].groupby('Name', as_index=False).sum().sort_values(by='Cost', ascending=False).head(10)"
      ],
      "metadata": {
        "id": "xyo4qO3XRTrS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 1"
      ],
      "metadata": {
        "id": "0wOQAZs5pc--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 1 Word Cloud for  Expensive restaurants\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud  # Import WordCloud class\n",
        "\n",
        "plt.figure(figsize=(15, 5))\n",
        "text = \" \".join(name for name in meta_df_1.sort_values('Cost', ascending=False).Name[:30])\n",
        "word_cloud = WordCloud(width=1400, height=1400, collocations=False, background_color='black').generate(text)\n",
        "plt.imshow(word_cloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7v_ESjsspbW7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "K5QZ13OEpz2H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Because it succinctly represents the most frequent terms in the text data, providing a visually appealing and intuitive way to grasp key themes or topics within the dataset."
      ],
      "metadata": {
        "id": "XESiWehPqBRc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "lQ7QKXXCp7Bj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insights identifying the most common words or terms present in Big letters that simplifies for expensive restaurants.\n"
      ],
      "metadata": {
        "id": "C_j1G7yiqdRP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n"
      ],
      "metadata": {
        "id": "448CDAPjqfQr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yes, It leading to a positive business impact through enhanced understanding of prevalent themes or topics."
      ],
      "metadata": {
        "id": "3cspy4FjqxJW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 2"
      ],
      "metadata": {
        "id": "KSlN3yHqYklG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 2 visualization code\n",
        "cuisine_counts = meta_df_1['Cuisines'].str.split(', ', expand=True).stack().value_counts()\n",
        "plt.figure(figsize=(10, 6))\n",
        "cuisine_counts.plot(kind='bar', color='skyblue')\n",
        "plt.title('Number of Restaurants per Cuisine')\n",
        "plt.xlabel('Cuisine')\n",
        "plt.ylabel('Number of Restaurants')\n",
        "plt.xticks(rotation=45)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "R4YgtaqtYklH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "t6dVpIINYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To visualize restaurant counts per cuisine."
      ],
      "metadata": {
        "id": "5aaW0BYyYklI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "meta_df_1.columns"
      ],
      "metadata": {
        "id": "32lSQBgD3VO6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "ijmpgYnKYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Chinese cuisine dominates with the highest number of restaurants."
      ],
      "metadata": {
        "id": "PSx9atu2YklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "-JiQyfWJYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Positive Impact:** Helps identify popular cuisines for targeted marketing.\n",
        "\n",
        "**Negative Growth**: Over-saturation of Chinese restaurants may lead to intense competition."
      ],
      "metadata": {
        "id": "BcBbebzrYklV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 3"
      ],
      "metadata": {
        "id": "EM7whBJCYoAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 3 visualization code\n",
        "top_restaurants = meta_df_1.nlargest(10, 'Cost')\n",
        "\n",
        "# Bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(top_restaurants['Name'], top_restaurants['Cost'], color='lightgreen')\n",
        "plt.title('Top 10 Restaurants by Cost')\n",
        "plt.xlabel('Restaurant')\n",
        "plt.ylabel('Cost (in Rupees)')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "t6GMdE67YoAp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "fge-S5ZAYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I picked the bar chart because it effectively visualizes the top 10 restaurants by their cost, allowing for easy comparison."
      ],
      "metadata": {
        "id": "5dBItgRVYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "85gYPyotYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight from the chart is that these top 10 restaurants have the highest cost among all restaurants, which may indicate their upscale nature or premium offerings."
      ],
      "metadata": {
        "id": "4jstXR6OYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "RoGjAbkUYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The gained insight can help in targeting high-spending customers and positioning the restaurant as a premium dining option, potentially leading to positive business impact by increasing revenue. However, it may also deter budget-conscious customers, potentially leading to negative growth in foot traffic or sales volume."
      ],
      "metadata": {
        "id": "zfJ8IqMcYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 4"
      ],
      "metadata": {
        "id": "4Of9eVA-YrdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 4 visualization code\n",
        "collection_counts = meta_df_1['Collections'].str.split(', ', expand=True).stack().value_counts()\n",
        "\n",
        "# Pie chart\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.pie(collection_counts, labels=collection_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "plt.title('Distribution of Restaurants by Collection')\n",
        "plt.axis('equal')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "irlUoxc8YrdO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "iky9q4vBYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the pie chart because it effectively illustrates the distribution of restaurants by their collections, providing a clear visualization of the proportion of each collection category."
      ],
      "metadata": {
        "id": "aJRCwT6DYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "F6T5p64dYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight from the chart is that \"Great Buffets\" and \"Hyderabad's Hottest\" are the most common collections among restaurants, followed by \"Food Hygiene Rated Restaurants in Hyderabad\" and others."
      ],
      "metadata": {
        "id": "Xx8WAJvtYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "y-Ehk30pYrdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Positive impact:** Insights from the pie chart can guide restaurants to align their offerings with popular collections, enhancing customer attraction and satisfaction.\n",
        "\n",
        "**Negative impact:** Overemphasis on certain collections may limit diversity and alienate customers seeking alternative dining experiences, potentially leading to reduced customer satisfaction and loyalty."
      ],
      "metadata": {
        "id": "jLNxxz7MYrdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 5"
      ],
      "metadata": {
        "id": "bamQiAODYuh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 5 visualization code\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.hist(meta_df_1['Cost'], bins=20, color='gold', edgecolor='black')\n",
        "plt.title('Distribution of Restaurant Costs')\n",
        "plt.xlabel('Cost (in Rupees)')\n",
        "plt.ylabel('Frequency')\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TIJwrbroYuh3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "QHF8YVU7Yuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the histogram because it effectively displays the distribution of restaurant costs, providing insights into the range and frequency of different cost levels."
      ],
      "metadata": {
        "id": "dcxuIMRPYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "GwzvFGzlYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Insights from the chart reveal the distribution of restaurant costs, indicating the most common price ranges and helping businesses understand market preferences."
      ],
      "metadata": {
        "id": "uyqkiB8YYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "qYpmQ266Yuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Positive impact: Analyzing restaurant cost distribution aids in optimizing pricing strategies to align with customer preferences, enhancing profitability.\n",
        "\n",
        "Negative impact: Concentration of costs at extreme price points suggests market segmentation issues or pricing discrepancies, potentially harming profitability."
      ],
      "metadata": {
        "id": "_WtzZ_hCYuh4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 6"
      ],
      "metadata": {
        "id": "OH-pJp9IphqM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 6 visualization code for Distribution of Rating\n",
        "plt.figure(figsize=(10, 5))\n",
        "sns.histplot(review_df_2['Rating'], bins=10, kde=True, color='skyblue')\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Frequency')\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "kuRf4wtuphqN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "bbFf2-_FphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the histogram with KDE plot because it provides a visual representation of the distribution of ratings and includes a kernel density estimate for smoothness."
      ],
      "metadata": {
        "id": "loh7H2nzphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "_ouA3fa0phqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight from the chart is that the majority of ratings cluster around the higher end, suggesting overall positive feedback for the restaurant."
      ],
      "metadata": {
        "id": "VECbqPI7phqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "Seke61FWphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Positive impact:**Understanding the distribution of ratings can guide strategic decisions to enhance customer satisfaction, potentially leading to increased patronage and positive business growth.\n",
        "\n",
        "**Negative impact:** If there's a significant concentration of low ratings, it may signal dissatisfaction among customers, potentially leading to decreased patronage and negative business growth."
      ],
      "metadata": {
        "id": "DW4_bGpfphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 7"
      ],
      "metadata": {
        "id": "PIIx-8_IphqN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 7 visualization code for Count of Reviews by Restaurant\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.countplot(x='Restaurant', data=review_df_2, order=review_df_2['Restaurant'].value_counts().index[:10])\n",
        "plt.title('Top 10 Restaurants by Number of Reviews')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "lqAIGUfyphqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "t27r6nlMphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the countplot because it effectively displays the distribution of reviews among the top 10 restaurants."
      ],
      "metadata": {
        "id": "iv6ro40sphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "r2jJGEOYphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight from the chart is the comparative number of reviews among the top 10 restaurants."
      ],
      "metadata": {
        "id": "Po6ZPi4hphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "b0JNsNcRphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Positive impact:** Identifying the most reviewed restaurants can help focus marketing efforts and improve customer engagement, potentially leading to increased foot traffic and positive business growth.\n",
        "\n",
        "**Negative impact:**If certain restaurants have significantly fewer reviews compared to others in the top 10, it may indicate a lack of popularity or customer satisfaction issues, potentially leading to negative growth in those establishments."
      ],
      "metadata": {
        "id": "xvSq8iUTphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 8"
      ],
      "metadata": {
        "id": "BZR9WyysphqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 8 visualization code for  Count ratings\n",
        "rating_counts = review_df_2['Rating'].value_counts().sort_index()\n",
        "\n",
        "# Bar chart\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.bar(rating_counts.index, rating_counts.values, color='skyblue')\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.xticks(rating_counts.index)\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TdPTWpAVphqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "jj7wYXLtphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I picked the bar chart because it effectively displays the distribution of ratings, which is a common metric for evaluating restaurant performance."
      ],
      "metadata": {
        "id": "Ob8u6rCTphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "eZrbJ2SmphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight from the chart is that most ratings fall within the higher range, indicating generally positive feedback for the restaurant."
      ],
      "metadata": {
        "id": "mZtgC_hjphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "rFu4xreNphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insights can help in understanding customer satisfaction levels, guiding improvements in service or menu offerings to enhance positive business impact. However, if there were a significant number of low ratings, it could indicate areas of concern needing attention to prevent negative growth."
      ],
      "metadata": {
        "id": "ey_0qi68phqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 9"
      ],
      "metadata": {
        "id": "YJ55k-q6phqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 9 visualization code for Pie chart of Ratings\n",
        "plt.figure(figsize=(5, 6))\n",
        "plt.pie(rating_counts, labels=rating_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.axis('equal')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "B2aS4O1ophqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "gCFgpxoyphqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the pie chart because it effectively illustrates the distribution of ratings as percentages of the whole."
      ],
      "metadata": {
        "id": "TVxDimi2phqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "OVtJsKN_phqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight from the chart is the proportional representation of each rating category."
      ],
      "metadata": {
        "id": "ngGi97qjphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "lssrdh5qphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Positive impact:**Understanding the distribution of ratings can help identify areas of improvement in products or services, leading to enhanced customer satisfaction and potentially increased business.\n",
        "\n",
        "**Negative impact:** If a significant portion of the ratings is low, it may indicate dissatisfaction among customers, potentially leading to negative growth due to declining sales or reputation damage."
      ],
      "metadata": {
        "id": "tBpY5ekJphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 10 - Correlation Heatmap"
      ],
      "metadata": {
        "id": "NC_X3p0fY2L0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,10))\n",
        "sns.heatmap(review_df_2.corr(), cmap ='PiYG', annot = True)\n",
        "\n"
      ],
      "metadata": {
        "id": "xyC9zolEZNRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "UV0SzAkaZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The correlation heatmap is suitable for identifying potential relationships between numerical variables, providing insights into how 'Rating' correlates with 'Metadata' in the dataset."
      ],
      "metadata": {
        "id": "DVPuT8LYZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "YPEH6qLeZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The insight is the correlation coefficient between 'Rating' and 'Metadata', indicating the strength and direction of their relationship."
      ],
      "metadata": {
        "id": "bfSqtnDqZNRR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***5. Hypothesis Testing***"
      ],
      "metadata": {
        "id": "g-ATYxFrGrvw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Based on your chart experiments, define three hypothetical statements from the dataset. In the next three questions, perform hypothesis testing to obtain final conclusion about the statements through your code and statistical testing."
      ],
      "metadata": {
        "id": "Yfr_Vlr8HBkt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on the dataset, three hypothetical statements emerge: a positive correlation between restaurant ratings and the number of reviews; a potential relationship between higher ratings and increased follower counts for restaurants; and the influence of metadata on restaurant ratings. To validate these hypotheses, statistical testing will be conducted, including Pearson correlation coefficient test, independent t-test for mean follower counts."
      ],
      "metadata": {
        "id": "-7MS06SUHkB-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hypothetical Statement - 1"
      ],
      "metadata": {
        "id": "8yEUt7NnHlrM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. State Your research hypothesis as a null hypothesis and alternate hypothesis."
      ],
      "metadata": {
        "id": "tEA2Xm5dHt1r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Null Hypothesis (H0):** The average rating for restaurants is 4.5.\n",
        "\n",
        "**Alternative Hypothesis (H1):**The average rating for restaurants is not 4.5."
      ],
      "metadata": {
        "id": "HI9ZP0laH0D-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Perform an appropriate statistical test."
      ],
      "metadata": {
        "id": "I79__PHVH19G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing Statistical Test to obtain P-Value\n",
        "import pandas as pd\n",
        "from scipy import stats\n",
        "data = pd.read_csv('/content/Zomato Restaurant reviews.csv')\n",
        "\n",
        "# Performing one-sample t-test\n",
        "t_stat, p_value = stats.ttest_1samp(data['Rating'], 4.5)\n",
        "t_stat, p_value\n",
        "# Convert non-numeric values in the 'Rating' column to NaN\n",
        "data['Rating'] = pd.to_numeric(data['Rating'], errors='coerce')\n",
        "\n",
        "# Drop rows with NaN values in the 'Rating' column\n",
        "data.dropna(subset=['Rating'], inplace=True)\n",
        "\n",
        "# Performing one-sample t-test\n",
        "t_stat, p_value = stats.ttest_1samp(data['Rating'], 4.5)\n",
        "(t_stat, p_value)"
      ],
      "metadata": {
        "id": "oZrfquKtyian"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which statistical test have you done to obtain P-Value?"
      ],
      "metadata": {
        "id": "Ou-I18pAyIpj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have performed a one-sample t-test to obtain the p-value for the hypothesis testing. The p-value was obtained as 0.0."
      ],
      "metadata": {
        "id": "s2U0kk00ygSB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Why did you choose the specific statistical test?"
      ],
      "metadata": {
        "id": "fF3858GYyt-u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The one-sample t-test was chosen because it's designed to compare the mean of a single sample to a known or hypothesized population mean, which in this case was to test if the average restaurant rating significantly differs from 4.5."
      ],
      "metadata": {
        "id": "HO4K0gP5y3B4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hypothetical Statement - 2"
      ],
      "metadata": {
        "id": "4_0_7-oCpUZd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. State Your research hypothesis as a null hypothesis and alternate hypothesis."
      ],
      "metadata": {
        "id": "hwyV_J3ipUZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Null Hypothesis (H0):** The average rating for restaurants with pictures is the same as those without pictures.\n",
        "\n",
        "**Alternative Hypothesis (H1):** The average rating for restaurants with pictures is different from those without pictures."
      ],
      "metadata": {
        "id": "FnpLGJ-4pUZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Perform an appropriate statistical test."
      ],
      "metadata": {
        "id": "3yB-zSqbpUZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n",
        "\n",
        "# Splitting the dataset into two groups based on the presence of pictures\n",
        "with_pictures = review_df_2[review_df_2['Pictures'] > 0]['Rating']\n",
        "without_pictures = review_df_2[review_df_2['Pictures'] == 0]['Rating']\n",
        "\n",
        "# Performing the independent t-test\n",
        "t_statistic, p_value = stats.ttest_ind(with_pictures, without_pictures, equal_var=False)\n",
        "\n",
        "# Checking the significance level\n",
        "alpha = 0.05\n",
        "if p_value < alpha:\n",
        "    print(\"Reject the null hypothesis: The average rating for restaurants with pictures is different from those without pictures.\")\n",
        "else:\n",
        "    print(\"Fail to reject the null hypothesis: There is no significant difference in the average rating between restaurants with and without pictures.\")\n"
      ],
      "metadata": {
        "id": "C_bRYUyWDM1G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which statistical test have you done to obtain P-Value?"
      ],
      "metadata": {
        "id": "dEUvejAfpUZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The statistical test performed to obtain the p-value is an independent samples t-test."
      ],
      "metadata": {
        "id": "oLDrPz7HpUZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Why did you choose the specific statistical test?"
      ],
      "metadata": {
        "id": "Fd15vwWVpUZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The independent samples t-test was chosen because it is suitable for comparing the means of two independent groups to determine if there is a statistically significant difference between them. In this case, the hypothesis is comparing the average ratings for restaurants with and without pictures, making the independent samples t-test appropriate for the analysis."
      ],
      "metadata": {
        "id": "4xOGYyiBpUZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hypothetical Statement - 3"
      ],
      "metadata": {
        "id": "bn_IUdTipZyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. State Your research hypothesis as a null hypothesis and alternate hypothesis."
      ],
      "metadata": {
        "id": "49K5P_iCpZyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Null Hypothesis (H0):**The average rating for restaurants with metadata is equal to 4.\n",
        "\n",
        "**Alternative Hypothesis (H1):** The average rating for restaurants with metadata is not equal to 4."
      ],
      "metadata": {
        "id": "7gWI5rT9pZyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Perform an appropriate statistical test."
      ],
      "metadata": {
        "id": "Nff-vKELpZyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform Statistical Test to obtain P-Value\n",
        "from scipy.stats import ttest_1samp\n",
        "\n",
        "# Selecting the 'Rating' column for restaurants with metadata\n",
        "ratings_with_metadata = review_df_2[review_df_2['Metadata'].notnull()]['Rating']\n",
        "\n",
        "# Perform one-sample t-test\n",
        "t_statistic, p_value = ttest_1samp(ratings_with_metadata, 4)\n",
        "\n",
        "# Print the results\n",
        "print(\"T-statistic:\", t_statistic)\n",
        "print(\"P-value:\", p_value)\n"
      ],
      "metadata": {
        "id": "s6AnJQjtpZyI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which statistical test have you done to obtain P-Value?"
      ],
      "metadata": {
        "id": "kLW572S8pZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The statistical test used to obtain the p-value in this case is the one-sample t-test."
      ],
      "metadata": {
        "id": "ytWJ8v15pZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Why did you choose the specific statistical test?"
      ],
      "metadata": {
        "id": "dWbDXHzopZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I chose the one-sample t-test because it is appropriate for comparing the mean of a single group (restaurants with metadata) to a known value (in this case, 4). It helps determine whether the observed mean rating significantly differs from the hypothesized mean."
      ],
      "metadata": {
        "id": "M99G98V6pZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***6. Feature Engineering & Data Pre-processing***"
      ],
      "metadata": {
        "id": "yLjJCtPM0KBk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Handling Missing Values"
      ],
      "metadata": {
        "id": "xiyOF9F70UgQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handle Duplicate Values from the dataset"
      ],
      "metadata": {
        "id": "Xi5_SOJn5RIu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Check the duplcate values persent in the data set\n",
        "print(f'Total number of Duplicate Value persent in Restuent datast set {meta_df_1.duplicated().sum()} \\n')\n",
        "print(f'Total number of Duplicate Value persent in Review datast set {review_df_2.duplicated().sum()}')"
      ],
      "metadata": {
        "id": "Ns3XXpa14xLf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Drop Duplicate values from the dataset\n",
        "\n",
        "review_df_2.drop_duplicates(inplace= True)\n",
        "\n",
        "# check the duplcate value after drop\n",
        "\n",
        "print(f'Total numer of Duplicate Value persent in Restuent datast set {meta_df_1.duplicated().sum()} \\n')\n",
        "print(f'Total numer of Duplicate Value persent in Review datast set {review_df_2.duplicated().sum()}')"
      ],
      "metadata": {
        "id": "jVPIPOmJ5T4C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Handle Missing values from Resturent Dataset**"
      ],
      "metadata": {
        "id": "z_sdTr2t5fU-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling Missing Values & Missing Value Imputation\n",
        "meta_df_1.isnull().sum()"
      ],
      "metadata": {
        "id": "0U1WeS7k5eew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fill null values with mode\n",
        "meta_df_1.Timings.fillna(resturent_df.Timings.mode()[0],inplace=True)\n",
        ""
      ],
      "metadata": {
        "id": "7onR45JQ5o8a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# check percentage of missing values  in Collections\n",
        "\n",
        "missing_percentage = ((resturent_df['Collections'].isnull().sum())/(len(meta_df_1['Collections'])))*100\n",
        "print(f'Percentage of missing value in Collections is {round(missing_percentage, 2)}%')\n",
        ""
      ],
      "metadata": {
        "id": "Tbs0XlDW5x9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# More then 50% of the data is missing so we are droping the colllection collumns\n",
        "meta_df_1.drop('Collections', axis=1, inplace = True)\n",
        "meta_df_1.isnull().sum()"
      ],
      "metadata": {
        "id": "RWR0GUri533j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Handle Missing values from Review Dataset**"
      ],
      "metadata": {
        "id": "6tZSr3ch6PFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.isnull().sum()"
      ],
      "metadata": {
        "id": "KmB01Zwy58OC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2[review_df_2['Reviewer'].isnull()]"
      ],
      "metadata": {
        "id": "l6aMhhi66Z61"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2['Restaurant'].dropna(inplace = True)"
      ],
      "metadata": {
        "id": "HwToXv2H6hse"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2 = review_df_2.dropna(subset=['Restaurant','Reviewer','Review'])\n",
        "review_df_2.isnull().sum()\n",
        ""
      ],
      "metadata": {
        "id": "fbPC6JIE6k67"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#filling null values in review and reviewer follower column\n",
        "review_df_2 = review_df_2.fillna({\"Review\": \"No Review\", \"Followers\": 0})\n",
        "review_df_2.isnull().sum()"
      ],
      "metadata": {
        "id": "2jhVla3S9S2C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "p7O44uMO9brs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### What all missing value imputation techniques have you used and why did you use those techniques?"
      ],
      "metadata": {
        "id": "7wuGOrhz0itI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We started with removing duplicte values persent in review dataset and handle missing vlaues persent in both resturent and Review dataset.\n",
        "\n",
        "Dataset that contains details about Resturent, had 1 null value in timing feature and more than 50% null value in collection feature. In order to treat with those I first replaced the null value for timing with mode since there was only one null and mode is robust to outliers plus that hotel name was one unique feature which had all other feature except timing and collection so it was better to preserve that data. Since there was more than 50% null values in collection feature, I removed the entire column because columns with a high percentage of null values are likely to have a lot of missing data, which can make it difficult to accurately analyze or make predictions based on the data.\n",
        "\n",
        "In the dataset tha has details of reviewer had Reviewer - 2, Review - 9, Rating - 2, Metadata - 2, Time - 2, Reviewer_Total_Review- 3, Reviewer_Followers - 1581, Review_Year - 2, Review_Month - 2, Review_Hour - 2. On analysing I found that feature like reviewer and reviewer total review had all null values, therefore I removed those two columns which made null values in other feature to zero except in review and reviewer followers columns. Since review was textual data, I changed those 7 null values to 'no review' and reviewer followers to 0 as follower is the meta data for reviewer and it can be 0.\n",
        "\n",
        "And thus all the null values were treated, at the end I then again merged both the dataset hotel and review dataset."
      ],
      "metadata": {
        "id": "1ixusLtI0pqI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Handling Outliers"
      ],
      "metadata": {
        "id": "id1riN9m0vUs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "review_df_2.columns"
      ],
      "metadata": {
        "id": "_sjpHR-fAinC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling Outliers & Outlier treatments\n",
        "resturent_df = meta_df_1.rename(columns = {'Name':'Restaurant'})\n",
        "merged_df = resturent_df.merge(review_df_2, on = 'Restaurant')\n",
        "merged_df.shape\n",
        "#Anamoly detection\n",
        "from sklearn.ensemble import IsolationForest\n",
        "#checking for normal distribution\n",
        "print(\"Skewness - Cost: %f\" % merged_df['Cost'].skew())\n",
        "print(\"Kurtosis - Cost: %f\" % merged_df['Cost'].kurt())\n",
        "# print(\"Skewness - Followers: %f\" % merged_df['Followers'].skew())\n",
        "# print(\"Kurtosis - Followers: %f\" % merged_df['Followers'].kurt())"
      ],
      "metadata": {
        "id": "M6w2CzZf04JK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter Plot of Cost\n",
        "plt.scatter(range(merged_df.shape[0]), np.sort(merged_df['Cost'].values))\n",
        "plt.xlabel('index')\n",
        "plt.ylabel('Cost')\n",
        "plt.title(\"Cost distribution\")\n",
        "sns.despine()\n",
        ""
      ],
      "metadata": {
        "id": "2gmyX_UaBakV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#distribution of cost\n",
        "sns.distplot(merged_df['Cost'])\n",
        "plt.title(\"Distribution of Cost\")\n",
        "sns.despine()\n",
        ""
      ],
      "metadata": {
        "id": "cRsSnNmJBf0t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#isolation forest for anamoly detection on cost\n",
        "isolation_forest = IsolationForest(n_estimators=100, contamination=0.01)\n",
        "isolation_forest.fit(merged_df['Cost'].values.reshape(-1, 1))\n",
        "merged_df['anomaly_score_univariate_Cost'] = isolation_forest.decision_function(merged_df['Cost'].values.reshape(-1, 1))\n",
        "merged_df['outlier_univariate_Cost'] = isolation_forest.predict(merged_df['Cost'].values.reshape(-1, 1))\n",
        ""
      ],
      "metadata": {
        "id": "zdl2kGyHBroM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#chart to visualize outliers\n",
        "xx = np.linspace(merged_df['Cost'].min(), merged_df['Cost'].max(), len(merged_df)).reshape(-1,1)\n",
        "anomaly_score = isolation_forest.decision_function(xx)\n",
        "outlier = isolation_forest.predict(xx)\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.plot(xx, anomaly_score, label='anomaly score')\n",
        "plt.fill_between(xx.T[0], np.min(anomaly_score), np.max(anomaly_score),\n",
        "where=outlier==-1, color='r',\n",
        "alpha=.4, label='outlier region')\n",
        "plt.legend()\n",
        "plt.ylabel('anomaly score')\n",
        "plt.xlabel('Cost')\n",
        "plt.show();"
      ],
      "metadata": {
        "id": "PvJvNFLbB5kr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Treating Outlier**"
      ],
      "metadata": {
        "id": "_Sp7V2DYCOUJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Handling Outliers & Outlier treatments\n",
        "# To separate the symmetric distributed features and skew symmetric distributed features\n",
        "symmetric_feature=[]\n",
        "non_symmetric_feature=[]\n",
        "for i in merged_df.describe().columns:\n",
        "  if abs(merged_df[i].mean()-merged_df[i].median())<0.2:\n",
        "    symmetric_feature.append(i)\n",
        "  else:\n",
        "    non_symmetric_feature.append(i)\n",
        "\n",
        "# Getting Symmetric Distributed Features\n",
        "print(\"Symmetric Distributed Features : -\",symmetric_feature)\n",
        "\n",
        "# Getting Skew Symmetric Distributed Features\n",
        "print(\"Skew Symmetric Distributed Features : -\",non_symmetric_feature)"
      ],
      "metadata": {
        "id": "hGLjZoLsCGmp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# For Skew Symmetric features defining upper and lower boundry\n",
        "#Outer Fence\n",
        "def outlier_treatment_skew(df,feature):\n",
        "  IQR= df[feature].quantile(0.75)- df[feature].quantile(0.25)\n",
        "  lower_bridge =df[feature].quantile(0.25)- 1.5*IQR\n",
        "  upper_bridge =df[feature].quantile(0.75)+ 1.5*IQR\n",
        "  # print(f'upper : {upper_bridge} lower : {lower_bridge}')\n",
        "  return upper_bridge,lower_bridge"
      ],
      "metadata": {
        "id": "GteedZuVCRG2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Restricting the data to lower and upper boundary for cost in hotel dataset\n",
        "#lower limit capping\n",
        "resturent_df.loc[resturent_df['Cost']<= outlier_treatment_skew(df=resturent_df, feature='Cost')[1], 'Cost']=outlier_treatment_skew(df=resturent_df,feature='Cost')[1]\n",
        "\n",
        "#upper limit capping\n",
        "resturent_df.loc[resturent_df['Cost']>= outlier_treatment_skew(df=resturent_df, feature='Cost')[0], 'Cost']=outlier_treatment_skew(df=resturent_df,feature='Cost')[0]\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "DVBnHr68CVB2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "##### What all outlier treatment techniques have you used and why did you use those techniques?"
      ],
      "metadata": {
        "id": "578E2V7j08f6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I  have found that  feature show positively skew distribution and using isolation forest they have outliers, So We have used capping technique instead of removing outliers, capped outliers with the highest and lowest limit using IQR method."
      ],
      "metadata": {
        "id": "uGZz5OrT1HH-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Categorical Encoding"
      ],
      "metadata": {
        "id": "89xtkJwZ18nB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode your categorical columns\n",
        "\n",
        "#categorial encoding using pd.getdummies\n",
        "#new df with important categories\n",
        "cluster_dummy = resturent_df[['Restaurant','Cuisines']]\n",
        "\n",
        "#spliting cuisines as they are separted with comma and converting into list\n",
        "cluster_dummy['Cuisines'] = cluster_dummy['Cuisines'].str.split(',')\n",
        "\n",
        "#using explode converting list to unique individual items\n",
        "cluster_dummy = cluster_dummy.explode('Cuisines')\n",
        "\n",
        "#removing extra trailing space from cuisines after exploded\n",
        "cluster_dummy['Cuisines'] = cluster_dummy['Cuisines'].apply(lambda x: x.strip())\n",
        "\n",
        "#using get dummies to get dummies for cuisines\n",
        "cluster_dummy = pd.get_dummies(cluster_dummy, columns=[\"Cuisines\"], prefix=[\"Cuisines\"])\n",
        "\n",
        "#checking if the values are correct\n",
        "cluster_dummy.loc[:, cluster_dummy.columns.str.startswith('Cuisines_')].idxmax(1)[:6]\n",
        "\n",
        "#replacing cuisines_ from columns name - for better understanding run seperatly\n",
        "cluster_dummy.columns = cluster_dummy.columns.str.replace(\"Cuisines_\",\"\")\n",
        "\n",
        "\n",
        "#grouping each restaurant as explode created unnecessary rows\n",
        "cluster_dummy = cluster_dummy.groupby(\"Restaurant\").sum().reset_index()"
      ],
      "metadata": {
        "id": "21JmIYMG2hEo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#total cuisine count\n",
        "resturent_df['Total_Cuisine_Count'] = resturent_df['Cuisines'].apply(lambda x : len(x.split(',')))"
      ],
      "metadata": {
        "id": "4hDh98ueDPB8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "resturent_df.head()"
      ],
      "metadata": {
        "id": "DsFr3XbLDRqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "cluster_dummy.head(2)\n",
        ""
      ],
      "metadata": {
        "id": "XFA4LhcKDVtH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#adding cost column to the new dataset\n",
        "cluster_dummy = resturent_df[['Restaurant','Cost','Total_Cuisine_Count']].merge(cluster_dummy, on = 'Restaurant')\n",
        "cluster_dummy.shape"
      ],
      "metadata": {
        "id": "bs69yp5rDYbG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### What all categorical encoding techniques have you used & why did you use those techniques?"
      ],
      "metadata": {
        "id": "67NQN5KX2AMe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have used one hot encoding on the cuisine category and based on the cuisine if present i gave value to 1 and if absent gave value of 0. Benefit of using one hot encoding:\n",
        "\n",
        "Handling categorical variables with no ordinal relationship:\n",
        "One-hot encoding does not assume any ordinal relationship between the categories, making it suitable for categorical features that do not have a natural ordering.\n",
        "\n",
        "Handling categorical variables with many unique values\n",
        "One-hot encoding can handle categorical features with a high cardinality, which can be useful when there are many unique categories.\n",
        "\n",
        "Handling categorical variables with multiple levels\n",
        "One-hot encoding can handle categorical features with multiple levels, such as \"state\" and \"city\". This can be useful when there are many unique combinations of levels.\n",
        "\n",
        "Handling categorical variables with missing values\n",
        "One-hot encoding can handle missing values by creating a new category for them.\n",
        "\n",
        "Model interpretability\n",
        "One-hot encoded features are easy to interpret as the encoded values are binary, thus making it easy to understand the relationship between the categorical feature and the target variable.\n",
        "\n",
        "Compatibility with many machine learning models\n",
        "One-hot encoded features are compatible with most machine learning models, including linear and logistic regression, decision trees, and neural networks."
      ],
      "metadata": {
        "id": "UDaue5h32n_G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Textual Data Preprocessing\n",
        "(It's mandatory for textual dataset i.e., NLP, Sentiment Analysis, Text Clustering etc.)"
      ],
      "metadata": {
        "id": "Iwf50b-R2tYG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Expand Contraction"
      ],
      "metadata": {
        "id": "GMQiZwjn3iu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# creating new df for text processing of sentiment analysis\n",
        "sentiment_df = review_df_2[['Reviewer','Restaurant','Rating','Review']]\n",
        "# analysing five random sample\n",
        "sentiment_df.sample(5)\n",
        ""
      ],
      "metadata": {
        "id": "PTouz10C3oNN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#setting index\n",
        "sentiment_df = sentiment_df.reset_index()\n",
        "sentiment_df['index'] = sentiment_df.index\n",
        "\n",
        "\n",
        "sentiment_df.sample(5)\n"
      ],
      "metadata": {
        "id": "76iFsGfXEfGp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        " # Install contractions\n",
        "!pip install contractions\n",
        "\n",
        "# import sys\n",
        "import sys\n",
        "!{sys.executable} -m pip install contractions\n",
        "\n",
        "# import contactions\n",
        "import contractions"
      ],
      "metadata": {
        "id": "Z2NOoj4iEisf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Expand Contraction\n",
        "\n",
        "# applying fuction for contracting text\n",
        "sentiment_df['Review']=sentiment_df['Review'].apply(lambda x:contractions.fix(x))"
      ],
      "metadata": {
        "id": "Bn5FANmEEt2a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Lower Casing"
      ],
      "metadata": {
        "id": "WVIkgGqN3qsr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lower Casing\n",
        "sentiment_df['Review'] = sentiment_df['Review'].str.lower()\n",
        "sentiment_df.head()\n",
        ""
      ],
      "metadata": {
        "id": "88JnJ1jN3w7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Removing Punctuations"
      ],
      "metadata": {
        "id": "XkPnILGE3zoT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Code to Remove Punctuations\n",
        "import string\n",
        "def remove_punctuation(text):\n",
        "  '''a function for removing punctuation'''\n",
        "\n",
        "  # replacing the punctuations with no space,\n",
        "  # which in effect deletes the punctuation marks\n",
        "  translator = str.maketrans('', '', string.punctuation)\n",
        "  # return the text stripped of punctuation marks\n",
        "  return text.translate(translator)\n",
        ""
      ],
      "metadata": {
        "id": "vqbBqNaA33c0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#remove punctuation using  Created function\n",
        "sentiment_df['Review'] = sentiment_df['Review'].apply(remove_punctuation)\n",
        "sentiment_df.sample(5)\n",
        ""
      ],
      "metadata": {
        "id": "ZRPvfmzpE3Zt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4. Removing URLs & Removing words and digits contain digits."
      ],
      "metadata": {
        "id": "Hlsf0x5436Go"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Import Library\n",
        "import re\n",
        "\n",
        "# Remove links\n",
        "sentiment_df[\"Review\"] = sentiment_df[\"Review\"].apply(lambda x: re.sub(r\"http\\S+\", \"\", x))\n",
        "\n",
        "# Remove digits\n",
        "sentiment_df[\"Review\"] = sentiment_df[\"Review\"].apply(lambda x: re.sub(r\"\\d+\", \"\", x))"
      ],
      "metadata": {
        "id": "2sxKgKxu4Ip3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#function to extract location of the restaurant\n",
        "def get_location(link):\n",
        "  link_elements = link.split(\"/\")\n",
        "  return link_elements[3]\n",
        "\n",
        "#create a location feature\n",
        "resturent_df['Location'] = resturent_df['Links'].apply(get_location)\n",
        "resturent_df.sample(2)"
      ],
      "metadata": {
        "id": "yL_iFfzzE-Oi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 5. Removing Stopwords & Removing White spaces"
      ],
      "metadata": {
        "id": "mT9DMSJo4nBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "\n",
        "from nltk.corpus import stopwords"
      ],
      "metadata": {
        "id": "T2LSJh154s8W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import stopwords"
      ],
      "metadata": {
        "id": "dseEUY0oFEro"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Remove Stopwords\n",
        "# extracting the stopwords from nltk library\n",
        "sw = stopwords.words('english')\n",
        ""
      ],
      "metadata": {
        "id": "5U9xmQ1VFGn_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# function call to remove Stopwords\n",
        "\n",
        "def remove_stopwords(text):\n",
        "  '''a function for removing the stopword'''\n",
        "  # removing the stop words and lowercasing the selected words\n",
        "  text = [word.lower() for word in text.split() if word.lower() not in sw]\n",
        "  # joining the list of words with space separator\n",
        "  return \" \".join(text)\n",
        "\n",
        "# Remove Stopwords\n",
        "sentiment_df['Review'] = sentiment_df['Review'].apply(remove_stopwords)\n",
        ""
      ],
      "metadata": {
        "id": "cDKZINsCFKCp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove White spaces\n",
        "sentiment_df['Review'] =sentiment_df['Review'].apply(lambda x: \" \".join(x.split()))\n",
        "\n",
        "#random sample\n",
        "sentiment_df.sample(2)"
      ],
      "metadata": {
        "id": "EgLJGffy4vm0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 6. Rephrase Text"
      ],
      "metadata": {
        "id": "c49ITxTc407N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# function to create rephrase sentence\n",
        "def rephrase_sentence(sentence):\n",
        "     # Tokenize the sentence\n",
        "     tokens = nltk.word_tokenize(sentence)\n",
        "\n",
        "     # Replace each token with its synonyms\n",
        "     new_sentence = []\n",
        "     for token in tokens:\n",
        "         synonyms = wordnet.synsets(token)\n",
        "         if synonyms:\n",
        "             new_sentence.append(synonyms[0].lemmas()[0].name())\n",
        "         else:\n",
        "             new_sentence.append(token)\n",
        "\n",
        "      #Join the tokens back into a sentence\n",
        "     rephrased_sentence = \" \".join(new_sentence)\n",
        "\n",
        "     return rephrased_sentence\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "foqY80Qu48N2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        " #apply the function to the 'Review' column\n",
        " sentiment_df['Review'] = sentiment_df['Review'].apply(rephrase_sentence)\n",
        " sentiment_df.sample(5)\n",
        ""
      ],
      "metadata": {
        "id": "pmPj56S7FVYn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 7. Tokenization"
      ],
      "metadata": {
        "id": "OeJFEK0N496M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Tokenization\n",
        "sentiment_df['Review'] = sentiment_df['Review'].apply(nltk.word_tokenize)\n",
        "sentiment_df.sample(5)"
      ],
      "metadata": {
        "id": "ijx1rUOS5CUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8. Text Normalization"
      ],
      "metadata": {
        "id": "9ExmJH0g5HBk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#applying Lemmatization\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "\n",
        "# Create a lemmatizer\n",
        "lemmatizer = WordNetLemmatizer()\n",
        "\n",
        "def lemmatize_tokens(tokens):\n",
        "    lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]\n",
        "    return lemmatized_tokens\n",
        "\n",
        "# Lemmatize the 'Review' column\n",
        "sentiment_df['Review'] = sentiment_df['Review'].apply(lemmatize_tokens)\n",
        "\n",
        "sentiment_df.sample(5)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "AIJ1a-Zc5PY8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which text normalization technique have you used and why?"
      ],
      "metadata": {
        "id": "cJNqERVU536h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I  have used Lemmatization as a text normalization technique.\n",
        "\n",
        "Lemmatization is the process of reducing words to their base or root form, similar to stemming. However, lemmatization uses a dictionary-based approach and considers the context of the word in order to determine its base form, while stemming uses simple heuristics and does not consider the context of the word. Lemmatization is a more accurate way of finding the root form of a word as it takes into account the context of the word as well as its grammatical structure.\n",
        "\n",
        "I have used lemmatization because it is a more accurate way of reducing words to their base form than stemming. Lemmatization considers the context of the word and its grammatical structure to determine its base form, which can help to improve the performance of natural language processing models. Lemmatization is often used in tasks such as text classification and information retrieval, where the meaning of the words is important."
      ],
      "metadata": {
        "id": "Z9jKVxE06BC1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Other Method for Normalization\n",
        "\n",
        "Tokenization is the process of breaking down a sentence or a piece of text into individual words or tokens. Tokenization is an important step in natural language processing as it allows us to work with individual words rather than the entire text.\n",
        "\n",
        "Stemming is the process of reducing words to their base or root form. This is useful in natural language processing because it allows us to reduce the dimensionality of the data by converting words to their common form. This can help improve the performance of models by reducing the number of unique words that need to be processed.\n",
        "\n",
        "Stemming can be used because they are common normalization techniques used in natural language processing to preprocess text data before it is fed into a model. Tokenization is the first step and it breaks down the text into individual words, which is necessary for most NLP tasks. Stemming is used to reduce the dimensionality of the data by converting words to their common form, this is useful for text classification and other NLP tasks where the meaning of the words is important.\n",
        "\n",
        "In general, stemming is a more aggressive technique that can remove more of the original word form, which may make it difficult for a lemmatizer to accurately identify the base form of the word. Additionally, some stemming algorithms may create non-real words, which are difficult for a lemmatizer to handle. Therefore, if the goal is to maintain the meaning of the text and preserve more of the original word forms, it would be more appropriate to apply lemmatization before stemming. However, if the goal is to reduce all words to their base forms and to group together different forms of the same word, it may be useful to try both ways and compare the results."
      ],
      "metadata": {
        "id": "nbKir3X6FtAO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 9. Part of speech tagging"
      ],
      "metadata": {
        "id": "k5UmGsbsOxih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# POS Taging\n",
        "sentiment_tfid = sentiment_df.copy()"
      ],
      "metadata": {
        "id": "btT3ZJBAO6Ik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Part-of-speech (POS) tagging can be important for sentiment analysis in some cases, as it can provide additional information about the structure and meaning of the text.\n",
        "\n",
        "For example, certain POS tags, such as adjectives and adverbs, are often used to express sentiment. By identifying these POS tags in the text, a sentiment analysis model can gain a better understanding of the sentiment being expressed. Additionally, certain grammatical structures, such as negations or modals, can change the sentiment of a sentence. By identifying these structures through POS tagging, a sentiment analysis model can take them into account when determining the overall sentiment of the text.\n",
        "\n",
        "However, it's worth noting that POS tagging is not always necessary for sentiment analysis. In some cases, a model may be able to achieve good performance without using POS tagging. Additionally, the complexity of a model that uses POS tagging increases, which could lead to longer training time and higher computational cost."
      ],
      "metadata": {
        "id": "WF59TKQVGGnR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from nltk import pos_tag\n",
        "nltk.download('averaged_perceptron_tagger')"
      ],
      "metadata": {
        "id": "DwHigI--F4Jm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_tfid['Review'] = sentiment_tfid['Review'].apply(nltk.pos_tag)\n",
        "sentiment_tfid.sample(5)\n",
        ""
      ],
      "metadata": {
        "id": "TS_LF6TSF72K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 10. Text Vectorization"
      ],
      "metadata": {
        "id": "T0VqWOYE6DLQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Vectorizing Text\n",
        "\n",
        "# Vectorizing Text\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "vectorizer = TfidfVectorizer(tokenizer=lambda x: x, lowercase=False)\n",
        "vectorizer.fit(sentiment_df['Review'].values)\n",
        "\n",
        "#creating independent variable for sentiment analysis\n",
        "X_tfidf = vectorizer.transform(sentiment_df['Review'].values)"
      ],
      "metadata": {
        "id": "yBRtdhth6JDE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "!pip install gensim\n",
        "import gensim\n",
        "from gensim import corpora"
      ],
      "metadata": {
        "id": "0jAX0IKpGNlR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Bag of Words\n",
        "tokenized_text = []\n",
        "for token in sentiment_df['Review']:\n",
        "    tokenized_text.append(token)\n",
        "\n",
        "#creating token dict\n",
        "tokens_dict = gensim.corpora.Dictionary(tokenized_text)"
      ],
      "metadata": {
        "id": "FS9Djuo_GPbz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#print token dict\n",
        "print(tokens_dict.token2id)"
      ],
      "metadata": {
        "id": "5HyqzaEDGSxN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#using tokens_dict.doc2bow() to generate BoW features for each tokenized course\n",
        "texts_bow = [tokens_dict.doc2bow(text) for text in tokenized_text]\n",
        "\n",
        "#creating a new text_bow dataframe based on the extracted BoW features\n",
        "tokens = []\n",
        "bow_values = []\n",
        "doc_indices = []\n",
        "doc_ids = []\n",
        "for text_idx, text_bow in enumerate(texts_bow):\n",
        "    for token_index, token_bow in text_bow:\n",
        "        token = tokens_dict.get(token_index)\n",
        "        tokens.append(token)\n",
        "        bow_values.append(token_bow)\n",
        "        doc_indices.append(text_idx)\n",
        "        doc_ids.append(sentiment_df[\"Restaurant\"][text_idx])\n",
        "\n",
        "bow_dict = {\"doc_index\": doc_indices,\n",
        "            \"doc_id\": doc_ids,\n",
        "            \"token\": tokens,\n",
        "            \"bow\": bow_values,\n",
        "            }\n",
        "bows_df = pd.DataFrame(bow_dict)\n",
        "bows_df.head()\n",
        ""
      ],
      "metadata": {
        "id": "Vgwp7aVQGdld"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which text vectorization technique have you used and why?"
      ],
      "metadata": {
        "id": "qBMux9mC6MCf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here I have used Tf-idf Vectorization technique.\n",
        "\n",
        "TF-IDF (term frequency-inverse document frequency) is a technique that assigns a weight to each word in a document. It is calculated as the product of the term frequency (tf) and the inverse document frequency (idf).\n",
        "\n",
        "The term frequency (tf) is the number of times a word appears in a document, while the inverse document frequency (idf) is a measure of how rare a word is across all documents in a collection. The intuition behind tf-idf is that words that appear frequently in a document but not in many documents across the collection are more informative and thus should be given more weight.\n",
        "\n",
        "The mathematical formula for tf-idf is as follows:\n",
        "\n",
        "tf-idf(t, d, D) = tf(t, d) * idf(t, D)\n",
        "\n",
        "where t is a term (word), d is a document, D is a collection of documents, tf(t, d) is the term frequency of t in d, and idf(t, D) is the inverse document frequency of t in D.\n",
        "\n",
        "The tf component of the weight assigns a value to a word based on how often it appears in the document, while the idf component assigns a value based on how rare the word is in the entire collection of documents. Tf-idf is commonly used in text classification and information retrieval tasks because it can help to down-weight the effect of common words and up-weight the effect of rare words which are more informative.\n",
        "\n",
        "It also helps to reduce the dimensionality of the data and increases the weight of important words, thus providing more informative and robust feature set for the model to work on.\n",
        "\n",
        "Text vectorization is the process of converting text data into numerical vectors that can be used as input for machine learning models.\n",
        "\n",
        "There are several ways to vectorize text data, one of the most common methods is using Tf-idf Vectorization, other methods are bag-of-words (BoW - uses CountVectorizer), word2vec, or doc2vec model."
      ],
      "metadata": {
        "id": "su2EnbCh6UKQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Feature Manipulation & Selection"
      ],
      "metadata": {
        "id": "-oLEiFgy-5Pf"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3t22LdqBGuYL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Feature Manipulation"
      ],
      "metadata": {
        "id": "C74aWNz2AliB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Resturant Dataset"
      ],
      "metadata": {
        "id": "Al0nIAniGvHQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Manipulate Features to minimize feature correlation and create new features\n",
        "# Manipulate Features to minimize feature correlation and create new features\n",
        "print(f' The number of row and columns persent in Resturent Dataset : {resturent_df.shape} , \\n ')\n",
        "print('--'*50, '\\n')\n",
        "\n",
        "print('All the variable persent in Resturent Dataset \\n')\n",
        "print(list(resturent_df.columns),  '\\n \\n')\n",
        "\n",
        "\n",
        "resturent_df.head()\n",
        ""
      ],
      "metadata": {
        "id": "h1qC4yhBApWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Review Dataset"
      ],
      "metadata": {
        "id": "K1emv9peHQau"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Manipulate Features to minimize feature correlation and create new features\n",
        "print(f' The number of row and columns persent in Resturent Dataset : {review_df_2.shape} , \\n ')\n",
        "print('--'*50, '\\n')\n",
        "\n",
        "print('All the variable persent in Resturent Dataset \\n')\n",
        "print(list(review_df_2.columns),  '\\n \\n')\n",
        "\n",
        "\n",
        "review_df_2.head()"
      ],
      "metadata": {
        "id": "cYcr3gZKG4N8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Feature Selection"
      ],
      "metadata": {
        "id": "2DejudWSA-a0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select your features wisely to avoid overfitting\n",
        "\n",
        "# All the column persent in Resturent Dataset\n",
        "resturent_df.columns"
      ],
      "metadata": {
        "id": "YLhe8UmaBCEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# All the column persent in cluster dummy Dataset\n",
        "cluster_dummy.columns\n",
        ""
      ],
      "metadata": {
        "id": "5ezSMW3VHmKj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# All the column persent in review Dataset\n",
        "review_df_2.columns\n",
        ""
      ],
      "metadata": {
        "id": "r9MyHoxuHogV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Feature Selected for clustering\n",
        "cluster_dummy.columns"
      ],
      "metadata": {
        "id": "FZocLlIoHxBn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#feature selected for sentiment analysis\n",
        "sentiment_df.columns"
      ],
      "metadata": {
        "id": "68rwsWZLH4h9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What all feature selection methods have you used  and why?"
      ],
      "metadata": {
        "id": "pEMng2IbBLp7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "What all feature selection methods have you used and why?\n",
        "We had used PCA for feature selection, which will be again beneficial for dimensional reduction, therefore will do the needfull in the precedding step.\n"
      ],
      "metadata": {
        "id": "rb2Lh6Z8BgGs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which all features you found important and why?"
      ],
      "metadata": {
        "id": "rAdphbQ9Bhjc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " The most important variables or features that capture the most variation in the data, and then to project the data onto a lower-dimensional space while preserving as much of the variance as possible."
      ],
      "metadata": {
        "id": "fGgaEstsBnaf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Data Transformation"
      ],
      "metadata": {
        "id": "TNVZ9zx19K6k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Do you think that your data needs to be transformed? If yes, which transformation have you used. Explain Why?"
      ],
      "metadata": {
        "id": "nqoHp30x9hH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform Your data\n",
        "# Getting symmetric and skew symmetric features from the cplumns\n",
        "symmetric_feature=[]\n",
        "non_symmetric_feature=[]\n",
        "for i in cluster_dummy.describe().columns:\n",
        "  if abs(cluster_dummy[i].mean()-cluster_dummy[i].median())<0.1:\n",
        "    symmetric_feature.append(i)\n",
        "  else:\n",
        "    non_symmetric_feature.append(i)\n",
        "\n",
        "# Getting Symmetric Distributed Features\n",
        "print(\"Symmetric Distributed Features : \",symmetric_feature)\n",
        "\n",
        "# Getting Skew Symmetric Distributed Features\n",
        "print(\"Skew Symmetric Distributed Features : \",non_symmetric_feature)\n",
        "\n",
        "\n",
        ""
      ],
      "metadata": {
        "id": "I6quWQ1T9rtH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#using log transformation to transform Cost as using capping tends to change median and mean\n",
        "cluster_dummy['Cost'] = np.log1p(cluster_dummy['Cost'])\n",
        "cluster_dummy['Cost'] = np.log1p(cluster_dummy['Cost'])"
      ],
      "metadata": {
        "id": "ZOnHc7ggJzYV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  Transform Your data\n",
        "for i,col in enumerate(['Cost']) :\n",
        "    sns.distplot(cluster_dummy[col], color = '#055E85');\n",
        "    feature = cluster_dummy[col]\n",
        "    plt.axvline(feature.mean(), color='#ff033e', linestyle='dashed', linewidth=3,label= 'mean');  #red\n",
        "    plt.axvline(feature.median(), color='#A020F0', linestyle='dashed', linewidth=3,label='median'); #cyan\n",
        "    plt.legend(bbox_to_anchor = (1.0, 1))\n",
        "    plt.title(f'{col.title()}');\n",
        "    plt.tight_layout();\n",
        ""
      ],
      "metadata": {
        "id": "2YSjTILOJ4-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Data Scaling"
      ],
      "metadata": {
        "id": "rMDnDkt2B6du"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling your data\n",
        "# Scaling your data\n",
        "cluster_dummy.sample(5)"
      ],
      "metadata": {
        "id": "dL9LWpySC6x_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "ZZpf9A_JKIbf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#normalizing numerical columns\n",
        "numerical_cols = ['Cost','Total_Cuisine_Count']\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(cluster_dummy[numerical_cols])\n",
        "scaled_df = cluster_dummy.copy()\n",
        "scaled_df[numerical_cols] = scaler.transform(cluster_dummy[numerical_cols])"
      ],
      "metadata": {
        "id": "jJLgSZwkKLDW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which method have you used to scale you data and why?"
      ],
      "metadata": {
        "id": "yiiVWRdJDDil"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have used StandardScaler to removes the mean and scales each feature/variable to unit variance."
      ],
      "metadata": {
        "id": "gEuLjr56Kf4p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. Dimesionality Reduction"
      ],
      "metadata": {
        "id": "1UUpS68QDMuG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Do you think that dimensionality reduction is needed? Explain Why?"
      ],
      "metadata": {
        "id": "kexQrXU-DjzY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yes, it is important to use dimensionality reduction techniques as dataset has 40 or more features. This is because, as the number of features increases, the computational cost of clustering algorithms also increases. In addition, high dimensionality can lead to the \"curse of dimensionality\", where the data becomes sparse and the clusters become harder to identify. Dimensionality reduction techniques such as PCA, t-SNE, or LLE can help reduce the number of features while maintaining the important information in the data, making it easier to cluster and interpret the results."
      ],
      "metadata": {
        "id": "GGRlBsSGDtTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# print sample dataset\n",
        "scaled_df.sample()"
      ],
      "metadata": {
        "id": "kQfvxBBHDvCa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.decomposition import PCA"
      ],
      "metadata": {
        "id": "hcU_pVKuLxsB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#applying pca to for the dimensionlty deduction\n",
        "\n",
        "features = scaled_df.columns\n",
        "features = features.drop('Restaurant')\n",
        "# create an instance of PCA\n",
        "pca = PCA()\n",
        "\n",
        "# fit PCA on features\n",
        "pca.fit(scaled_df[features])\n",
        ""
      ],
      "metadata": {
        "id": "ErGDmZ-oLy65"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.\n",
        "On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.**"
      ],
      "metadata": {
        "id": "5fYQWlktL6WP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#explained variance v/s no. of components\n",
        "plt.plot(np.cumsum(pca.explained_variance_ratio_), marker ='o', color = 'orange')\n",
        "plt.xlabel('number of components',size = 15, color = 'red')\n",
        "plt.ylabel('cumulative explained variance',size = 14, color = 'blue')\n",
        "plt.title('Variance v/s No. of Components',size = 20, color = 'green')\n",
        "plt.xlim([0, 8])\n",
        "plt.show()\n",
        ""
      ],
      "metadata": {
        "id": "88vkLh4hMDpq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#using n_component as 3\n",
        "pca = PCA(n_components=3)\n",
        "\n",
        "# fit PCA on features\n",
        "pca.fit(scaled_df[features])\n",
        "\n",
        "# explained variance ratio of each principal component\n",
        "print('Explained variation per principal component: {}'.format(pca.explained_variance_ratio_))\n",
        "# variance explained by three components\n",
        "print('Cumulative variance explained by 3 principal components: {:.2%}'.format(\n",
        "                                        np.sum(pca.explained_variance_ratio_)))\n",
        "\n",
        "# transform data to principal component space\n",
        "df_pca = pca.transform(scaled_df[features])"
      ],
      "metadata": {
        "id": "yWBT1QekMHUx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#shape\n",
        "print(\"original shape: \", scaled_df.shape)\n",
        "print(\"transformed shape:\", df_pca.shape)\n",
        ""
      ],
      "metadata": {
        "id": "3a4fnQOKMK6O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "UMRRhdB1MNFK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which dimensionality reduction technique have you used and why? (If dimensionality reduction done on dataset.)"
      ],
      "metadata": {
        "id": "T5CmagL3EC8N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have used PCA as dimension reduction technique, because PCA (Principal Component Analysis) is a widely used dimensionality reduction technique because it is able to identify patterns in the data that are responsible for the most variation. These patterns, known as principal components, are linear combinations of the original features that are uncorrelated with each other. By using the first few principal components, which account for the majority of the variation in the data, one can effectively reduce the dimensionality of the data while maintaining most of the important information.\n",
        "\n",
        "Another advantage of PCA is that it is a linear technique, which means it can be applied to data that have a linear relationship between features. It is also easy to interpret the results as the principal components can be thought of as new, uncorrelated features. Additionally, PCA can be used for data visualization by projecting high-dimensional data onto a 2D or 3D space for easy visualization.\n",
        "\n",
        "When PCA is applied before k-means, it is used to reduce the dimensionality of the data by transforming the original feature space into a new feature space of uncorrelated principal components. The k-means algorithm is then applied to the transformed data, resulting in clusters that are defined in the new feature space. The advantage of this approach is that it can help to remove noise and correlated features from the data, which can make the clustering results more interpretable. However, it also means that the clusters may be harder to interpret in the original feature space.\n",
        "\n",
        "When PCA is applied after k-means, it is used to visualize the clusters in a lower-dimensional space. The k-means algorithm is applied to the original data, resulting in clusters that are defined in the original feature space. PCA is then used to project the data into a lower-dimensional space, making it easier to visualize and interpret the clusters. The advantage of this approach is that the clusters can be easily interpreted in the original feature space. However, it may not be as effective in removing noise and correlated features from the data."
      ],
      "metadata": {
        "id": "ZKr75IDuEM7t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8. Data Splitting"
      ],
      "metadata": {
        "id": "BhH2vgX9EjGr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Split your data to train and test. Choose Splitting ratio wisely.\n",
        "#from text vectorization\n",
        "\n",
        "X = X_tfidf\n",
        "y = sentiment_df['Sentiment']"
      ],
      "metadata": {
        "id": "0CTyd2UwEyNM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "sentiment_df.shape\n",
        ""
      ],
      "metadata": {
        "id": "icvcVpdbM6gb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#spliting test train\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)\n",
        "\n",
        "# describes info about train and test set\n",
        "print(\"Number transactions X_train dataset: \", X_train.shape)\n",
        "print(\"Number transactions y_train dataset: \", y_train.shape)\n",
        "print(\"Number transactions X_test dataset: \", X_test.shape)\n",
        "print(\"Number transactions y_test dataset: \", y_test.shape)"
      ],
      "metadata": {
        "id": "ZiWsrZFhNh9p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What data splitting ratio have you used and why?"
      ],
      "metadata": {
        "id": "qjKvONjwE8ra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have used 80:20 split which is one the most used split ratio. Since there was only 9961 data, therefore I have used more in training set."
      ],
      "metadata": {
        "id": "Y2lJ8cobFDb_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9. Handling Imbalanced Dataset"
      ],
      "metadata": {
        "id": "P1XJ9OREExlT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Do you think the dataset is imbalanced? Explain Why."
      ],
      "metadata": {
        "id": "VFOzZv6IFROw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using class imbalance ratio (CIR) to measure data imbalance. The CIR is calculated as the ratio of the number of observations in the majority class (Nm) to the number of observations in the minority class (Nm). The CIR can be expressed as: CIR = Nm / Ns, where Nm is the number of observations in the majority class and Ns is the number of observations in the minority class."
      ],
      "metadata": {
        "id": "GeKDIv7pFgcC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#getting the value count for target class\n",
        "vc = sentiment_df.sentiment.value_counts().reset_index().rename(columns = {'index':'Sentiment','Sentiment':'Count'})\n",
        ""
      ],
      "metadata": {
        "id": "nQsRhhZLFiDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#defining majority and minority class value\n",
        "majority_class = vc.Count[0]\n",
        "minority_class = vc.Count[1]\n",
        ""
      ],
      "metadata": {
        "id": "KxFntQEWOixJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#calculating cir value for checking class imbalance\n",
        "CIR = majority_class / minority_class\n",
        "CIR"
      ],
      "metadata": {
        "id": "nJFJUOWCOqFc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Dependant Variable Column Visualization\n",
        "sentiment_df['Sentiment'].value_counts().plot(kind='pie',figsize=(15,6),autopct=\"%1.1f%%\",startangle=90,shadow=True,\n",
        "                                              labels=['Positive Sentiment','Negative Sentiment'],colors=['purple','green'],explode=[0.01,0.02])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "lVLcucG5Ouzd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What technique did you use to handle the imbalance dataset and why? (If needed to be balanced)"
      ],
      "metadata": {
        "id": "TIqpNgepFxVj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have found that there is imbalance in dataset with 63: 37 ratio, where 63 is the majaority class and 37 is the minority class. Even the CIR score suggest that majority class is 1.73 times greater than minority class. However it is considered as slight imbalance, therefore not performing any under or over sampling technique i.e., not required to treat class imabalance."
      ],
      "metadata": {
        "id": "qbet1HwdGDTz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***7. ML Model Implementation***"
      ],
      "metadata": {
        "id": "VfCC591jGiD4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Model - 1"
      ],
      "metadata": {
        "id": "OB4l2ZhMeS1U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 1 Implementation\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "7ebyywQieS1U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Explain the ML Model used and it's performance using Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "ArJBuiUVfxKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing evaluation Metric Score chart"
      ],
      "metadata": {
        "id": "rqD5ZohzfxKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Cross- Validation & Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "4qY1EAkEfxKe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 1 Implementation with hyperparameter optimization techniques (i.e., GridSearch CV, RandomSearch CV, Bayesian Optimization etc.)\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "Dy61ujd6fxKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which hyperparameter optimization technique have you used and why?"
      ],
      "metadata": {
        "id": "PiV4Ypx8fxKe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "negyGRa7fxKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "TfvqoZmBfxKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "OaLui8CcfxKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Model - 2"
      ],
      "metadata": {
        "id": "dJ2tPlVmpsJ0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Explain the ML Model used and it's performance using Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "JWYfwnehpsJ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing evaluation Metric Score chart"
      ],
      "metadata": {
        "id": "yEl-hgQWpsJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Cross- Validation & Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "-jK_YjpMpsJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 1 Implementation with hyperparameter optimization techniques (i.e., GridSearch CV, RandomSearch CV, Bayesian Optimization etc.)\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "Dn0EOfS6psJ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which hyperparameter optimization technique have you used and why?"
      ],
      "metadata": {
        "id": "HAih1iBOpsJ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "9kBgjYcdpsJ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "zVGeBEFhpsJ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "74yRdG6UpsJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Explain each evaluation metric's indication towards business and the business impact pf the ML model used."
      ],
      "metadata": {
        "id": "bmKjuQ-FpsJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "BDKtOrBQpsJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Model - 3"
      ],
      "metadata": {
        "id": "Fze-IPXLpx6K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 3 Implementation\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "FFrSXAtrpx6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Explain the ML Model used and it's performance using Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "7AN1z2sKpx6M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing evaluation Metric Score chart"
      ],
      "metadata": {
        "id": "xIY4lxxGpx6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Cross- Validation & Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "9PIHJqyupx6M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 3 Implementation with hyperparameter optimization techniques (i.e., GridSearch CV, RandomSearch CV, Bayesian Optimization etc.)\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "eSVXuaSKpx6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which hyperparameter optimization technique have you used and why?"
      ],
      "metadata": {
        "id": "_-qAgymDpx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "lQMffxkwpx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "Z-hykwinpx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "MzVzZC6opx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Which Evaluation metrics did you consider for a positive business impact and why?"
      ],
      "metadata": {
        "id": "h_CCil-SKHpo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "jHVz9hHDKFms"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Which ML model did you choose from the above created models as your final prediction model and why?"
      ],
      "metadata": {
        "id": "cBFFvTBNJzUa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "6ksF5Q1LKTVm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Explain the model which you have used and the feature importance using any model explainability tool?"
      ],
      "metadata": {
        "id": "HvGl1hHyA_VK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "YnvVTiIxBL-C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Congrats! Your model is successfully created and ready for deployment on a live server for a real user interaction !!!***"
      ],
      "metadata": {
        "id": "-Kee-DAl2viO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**"
      ],
      "metadata": {
        "id": "gCX9965dhzqZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Write the conclusion here."
      ],
      "metadata": {
        "id": "Fjb1IsQkh3yE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Hurrah! You have successfully completed your Machine Learning Capstone Project !!!***"
      ],
      "metadata": {
        "id": "gIfDvo9L0UH2"
      }
    }
  ]
}