Colab_Version.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VRyBBcjwfcAm"
   },
   "source": [
    "# Game Content Based Recommendation System"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qvF4nk3GfcAn"
   },
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from google.colab import files\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q1u5JZfHfcAq"
   },
   "outputs": [],
   "source": [
    "#Defining a helper function to get the title from the index\n",
    "\n",
    "def get_title_from_index(index):\n",
    "    return df[df.index == index][\"name\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-QRh41Y6fcAs"
   },
   "outputs": [],
   "source": [
    "#Defining a helper function to get the index value of the game using its title\n",
    "\n",
    "def get_index_from_title(name):\n",
    "    return df[df.name == name][\"index\"].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 91,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "headers": [
        [
         "content-type",
         "application/javascript"
        ]
       ],
       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "1S6K0pHBfcAu",
    "outputId": "b35b484f-9195-4a1b-b94b-4be0e5c6844d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Upload the dataset\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-bb6a4c4e-dcf5-4c37-970d-09cda613c182\" name=\"files[]\" multiple disabled\n",
       "        style=\"border:none\" />\n",
       "     <output id=\"result-bb6a4c4e-dcf5-4c37-970d-09cda613c182\">\n",
       "      Upload widget is only available when the cell has been executed in the\n",
       "      current browser session. Please rerun this cell to enable.\n",
       "      </output>\n",
       "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving steam.csv to steam.csv\n"
     ]
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "print(\"Upload the dataset\")\n",
    "data_to_load = files.upload()\n",
    "df = pd.read_csv(io.BytesIO(data_to_load['steam.csv']), encoding = \"ISO-8859-1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "eNiLsQyJfcAw",
    "outputId": "18754ce7-c14f-476f-8e61-b19c1c6f7e72"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'name', 'publisher', 'categories', 'genres', 'steamspy_tags'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Listing all the columns in the dataset\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bn7iWJ50fcAz"
   },
   "outputs": [],
   "source": [
    "#Selecting the features that are relevant to the system\n",
    "\n",
    "features = ['steamspy_tags', 'genres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "txgRZZQdfcA1"
   },
   "outputs": [],
   "source": [
    "#Cleaning data\n",
    "\n",
    "df = df.drop_duplicates()\n",
    "for feature in features:\n",
    "    df[feature] = df[feature].fillna(\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uvdYVVTIfcA3"
   },
   "outputs": [],
   "source": [
    "#Combines the features \n",
    "\n",
    "def combine_features(row):\n",
    "    return row['steamspy_tags'] + \" \" + row['genres']\n",
    "\n",
    "df[\"combined_features\"] = df.apply(combine_features,axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NtrgutFJfcA5"
   },
   "outputs": [],
   "source": [
    "#Creating Count matrix with sklearn countvectorizer\n",
    "\n",
    "cv = CountVectorizer()\n",
    "count_matrix = cv.fit_transform(df[\"combined_features\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aWsVNt1UfcA7"
   },
   "outputs": [],
   "source": [
    "#Computing the Cosine Similarity based on the count_matrix using sklearn cosine_similarity\n",
    "\n",
    "cosine_sim = cosine_similarity(count_matrix) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Go8AiPsXfcA9",
    "outputId": "fa1ac724-f86e-4dc3-e491-4ec4b7a84e63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the name of the GameGrand Theft Auto IV\n"
     ]
    }
   ],
   "source": [
    "#Users input: Look throught the csv file for the games.\n",
    "# Example: Grand Theft Auto IV\n",
    "\n",
    "Game_user_likes = input(\"Enter the name of the Game\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eMYES4SsfcA_"
   },
   "outputs": [],
   "source": [
    "#Getting index of movie with title\n",
    "\n",
    "Game_index = get_index_from_title(Game_user_likes)\n",
    "similar_Games =  list(enumerate(cosine_sim[Game_index]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ro_-ekAZfcBB"
   },
   "outputs": [],
   "source": [
    "#Getting list of similar movie in decending order of similarity score\n",
    "\n",
    "sorted_similar_Games = sorted(similar_Games,key=lambda x:x[1],reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "colab_type": "code",
    "id": "EdvWnVM8fcBD",
    "outputId": "e1ba98f8-288b-42fc-8b84-c7a3b0756b96"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grand Theft Auto: San Andreas\n",
      "Grand Theft Auto: Episodes from Liberty City\n",
      "Just Cause 2: Multiplayer Mod\n",
      "Grand Theft Auto V\n",
      "GUN\n",
      "Just Cause\n",
      "Grand Theft Auto III\n",
      "Grand Theft Auto: Vice City\n",
      "Grand Theft Auto\n",
      "Grand Theft Auto 2\n",
      "Far Cry 2: Fortune's Edition\n"
     ]
    }
   ],
   "source": [
    "#Printing the list in first 10 similar games\n",
    "\n",
    "i=0\n",
    "for element in sorted_similar_Games:\n",
    "    i=i+1\n",
    "    print (get_title_from_index(element[0]))\n",
    "    if i>10:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "67ueA9gpfcBF"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "name": "Colab_version.ipynb",
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   "language": "python",
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