Copy of Copy of Assignment_Regression Ver_2 Rishav Mitra .ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1VkVSOv7r9W7--_IdQv9d1XMGX0cA_yd0","timestamp":1690666056352},{"file_id":"1dUz83nQo4kdHlX8nnyICqhiKfB-E2QAd","timestamp":1690622569734}]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# The first part of the assignment requires you to perform multivariate regression to estimate the pricing of house using the availability facility information as features."],"metadata":{"id":"0lhMgGLznjx5"}},{"cell_type":"markdown","source":["**A peek into the dataset :**\n","\n"," The Boston Housing Dataset is a derived from information collected by the U.S. Census Service concerning housing in the area of Boston MA. The following describes the dataset columns:\n","\n","* CRIM - per capita crime rate by town\n","* ZN - proportion of residential land zoned for lots over 25,000 sq.ft.\n","* INDUS - proportion of non-retail business acres per town.\n","* CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n","* NOX - nitric oxides concentration (parts per 10 million)\n","* RM - average number of rooms per dwelling\n","* AGE - proportion of owner-occupied units built prior to 1940\n","* DIS - weighted distances to five Boston employment centres\n","* RAD - index of accessibility to radial highways\n","* TAX - full-value property-tax rate per \\$10,000\n","* PTRATIO - pupil-teacher ratio by town\n","* B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n","* LSTAT - % lower status of the population\n","* MEDV - Median value of owner-occupied homes in $1000's\n","\n","MEDV is the dependent variable.\n","\n","\n"," Using this dataset, explain your understanding of linear regression. You should do some checks on the features and the dependent variable, get some plots and distributions for the given variables."],"metadata":{"id":"iILHmbSLnOxh"}},{"cell_type":"code","source":["!pip install scikit-learn==1.0.2"],"metadata":{"id":"B8Et48LWveXh","executionInfo":{"status":"ok","timestamp":1690615531937,"user_tz":-330,"elapsed":7419,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"bf4f5b00-c34e-4701-97b1-b614396bdfde","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: scikit-learn==1.0.2 in /usr/local/lib/python3.10/dist-packages (1.0.2)\n","Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (1.22.4)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (1.10.1)\n","Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (1.3.1)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.0.2) (3.2.0)\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hPu0aFpLk5gp"},"outputs":[],"source":["import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","from sklearn.preprocessing import MinMaxScaler\n","from sklearn.model_selection import train_test_split\n","from sklearn.linear_model import LinearRegression\n","from sklearn.metrics import r2_score\n","\n","%matplotlib inline\n","\n","from sklearn.datasets import load_boston\n","import seaborn as sns"]},{"cell_type":"code","source":["# Load the dataset by calling the load_boston function\n","boston = load_boston()"],"metadata":{"id":"GwBX_gZgo0_k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690615543071,"user_tz":-330,"elapsed":618,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"040aab97-7a9b-459b-b5d9-3fccf29a6a0e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n","\n","    The Boston housing prices dataset has an ethical problem. You can refer to\n","    the documentation of this function for further details.\n","\n","    The scikit-learn maintainers therefore strongly discourage the use of this\n","    dataset unless the purpose of the code is to study and educate about\n","    ethical issues in data science and machine learning.\n","\n","    In this special case, you can fetch the dataset from the original\n","    source::\n","\n","        import pandas as pd\n","        import numpy as np\n","\n","\n","        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n","        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n","        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n","        target = raw_df.values[1::2, 2]\n","\n","    Alternative datasets include the California housing dataset (i.e.\n","    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n","    dataset. You can load the datasets as follows::\n","\n","        from sklearn.datasets import fetch_california_housing\n","        housing = fetch_california_housing()\n","\n","    for the California housing dataset and::\n","\n","        from sklearn.datasets import fetch_openml\n","        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n","\n","    for the Ames housing dataset.\n","    \n","  warnings.warn(msg, category=FutureWarning)\n"]}]},{"cell_type":"code","source":["# Find the keys of all features in the dataset\n","boston.feature_names\n","print(boston.feature_names)"],"metadata":{"id":"7hjuR9xopHrE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690615547789,"user_tz":-330,"elapsed":637,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"831d0378-0d2e-4535-cc78-3e28065c7b71"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['CRIM' 'ZN' 'INDUS' 'CHAS' 'NOX' 'RM' 'AGE' 'DIS' 'RAD' 'TAX' 'PTRATIO'\n"," 'B' 'LSTAT']\n"]}]},{"cell_type":"code","source":["# Find the type of the data\n","data=boston.data\n","# Fill\n","print(type(data))"],"metadata":{"id":"n5ZKpwceqWlI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690615554834,"user_tz":-330,"elapsed":595,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"f38c7baa-8999-494e-fa63-e635c39524b7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'numpy.ndarray'>\n"]}]},{"cell_type":"code","source":["data.shape\n","# this 506 represents the number of rows and 13 represents the number of columns in the boston dataset"],"metadata":{"id":"3FJRlWgKqkxi","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690615557614,"user_tz":-330,"elapsed":580,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"47107113-ff77-415a-b68b-b51139184ed4"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(506, 13)"]},"metadata":{},"execution_count":6}]},{"cell_type":"code","source":["# Load the data into a pandas dataframe\n","data_df = pd.DataFrame(data=data, columns=boston.feature_names)\n","# Print the head of the dataframe to see how it is\n","data_df['target'] = pd.Series(boston.target)\n","\n","# Fill\n","print(data_df.head())\n"],"metadata":{"id":"i6lF03VArDQR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1690615561399,"user_tz":-330,"elapsed":633,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"6b349527-5e3e-4da2-c2fd-6d6b1de9e1f7"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n","0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n","1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n","2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n","3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n","4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n","\n","   PTRATIO       B  LSTAT  target  \n","0     15.3  396.90   4.98    24.0  \n","1     17.8  396.90   9.14    21.6  \n","2     17.8  392.83   4.03    34.7  \n","3     18.7  394.63   2.94    33.4  \n","4     18.7  396.90   5.33    36.2  \n"]}]},{"cell_type":"code","source":["# sklearn datasets have the y value as boston.target. Load the y values as a new column data_df['PRICE']\n","# Fill\n","y = boston.target\n","data_df['PRICE'] = y\n","data_df.head()\n","# this y is basically now my training dataset"],"metadata":{"id":"T9CgQZj-rZl_","colab":{"base_uri":"https://localhost:8080/","height":206},"executionInfo":{"status":"ok","timestamp":1690615577353,"user_tz":-330,"elapsed":1082,"user":{"displayName":"Rishav Mitra","userId":"02783828769131760997"}},"outputId":"997b7d1f-9d27-41c5-c64e-43547467aa3e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n","0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n","1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n","2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n","3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n","4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n","\n","   PTRATIO       B  LSTAT  target  PRICE  \n","0     15.3  396.90   4.98    24.0   24.0  \n","1     17.8  396.90   9.14    21.6   21.6  \n","2     17.8  392.83   4.03    34.7   34.7  \n","3     18.7  394.63   2.94    33.4   33.4  \n","4     18.7  396.90   5.33    36.2   36.2  "],"text/html":["\n","\n","  <div id=\"df-8c39922a-e596-4275-8bd4-71831660334e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>B</th>\n","      <th>LSTAT</th>\n","      <th>target</th>\n","      <th>PRICE</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00632</td>\n","      <td>18.0</td>\n","      <td>2.31</td>\n","      <td>0.0</td>\n","      <td>0.538</td>\n","      <td>6.575</td>\n","      <td>65.2</td>\n","      <td>4.0900</td>\n","      <td>1.0</td>\n","      <td>296.0</td>\n","      <td>15.3</td>\n","      <td>396.90</td>\n","      <td>4.98</td>\n","      <td>24.0</td>\n","      <td>24.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.02731</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>6.421</td>\n","      <td>78.9</td>\n","      <td>4.9671</td>\n","      <td>2.0</td>\n","      <td>242.0</td>\n","      <td>17.8</td>\n","      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https://www.kaggle.com/rakeshrau/social-network-ads\n","!wget \"https://drive.google.com/uc?id=15WAD9_4CpUK6EWmgWVXU8YMnyYLKQvW8&export=download\" -O data.csv -q"],"metadata":{"id":"xqB6d8BGxgJZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Load the dataset using load_csv function and visualize the data\n","\n","def load_csv(https://drive.google.com/uc?id=15WAD9_4CpUK6EWmgWVXU8YMnyYLKQvW8&export=download):\n","    with pd.read_csv(https://drive.google.com/uc?id=15WAD9_4CpUK6EWmgWVXU8YMnyYLKQvW8&export=download , encoding='utf-8') as df:\n","        return df\n","\n","df = load_csv(https://drive.google.com/uc?id=15WAD9_4CpUK6EWmgWVXU8YMnyYLKQvW8&export=download)\n"],"metadata":{"id":"h5Dkd8pZxojl"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Try plotting a scatter plot of the data\n","%matplotlib inline\n","plt.scatter(df.User ID, df.Purchased, color = 'red')"],"metadata":{"id":"2YZc443px1OH"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Create the train test split\n","x = df[['Age']]\n","y = df['Purchased']\n","x_train, y_train, x_test, y_test = train_test_split(x,y, test_size= 0.15, )\n"],"metadata":{"id":"RlhbfI6nx9qT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Import the logistic regression model from sklearn and initialize it\n","from sklearn.linear_model import LogisticRegression\n","logreg = LogisticRegression()"],"metadata":{"id":"WLLTx_y4yCAR"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Fit the model using the training samples\n","logreg.fit(x_train, y_train)"],"metadata":{"id":"yLyRACISyPII"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Predict the y values for test_x values\n","y_pred = logreg.predict(x_test)"],"metadata":{"id":"ktGiUi60yUlB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Calculate the accuracy of the model using sklearn function\n","from sklearn.metrics import accuracy_score\n","accuracy = accuracy_score(y_test, y_pred)\n","print(\"Accuracy:\", accuracy)"],"metadata":{"id":"rr6bDgfhyciv"},"execution_count":null,"outputs":[]}]}