Task 2/Movie Rating Prediction - Task 3 .ipynb

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    "# Movie Rating Prediction With Python\n",
    "\n",
    "\n",
    "# Problem Statement\n",
    "\n",
    "1.Build a model that predicts the rating of a movie based on genre, director, and actors. You can use regression techniques to tackle this problem.\n",
    "\n",
    "2.The goal is to analyze historical movie data and develop a model that accurately estimates the rating given to a movie by users or critics.\n",
    "\n",
    "3.Movie rating prediction project enables you to explore data analysis, preprocessing, feature engineering and machine learning modeling techniques.It provides insights into the factors that infulence movie ratings and allows you to build a model that can estimate the ratings of movie accurately.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "21477b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8aeab8f0",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movieid</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men (1995)</td>\n",
       "      <td>Comedy|Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale (1995)</td>\n",
       "      <td>Comedy|Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II (1995)</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Movieid                               Title                        Genres\n",
       "0        1                    Toy Story (1995)   Animation|Children's|Comedy\n",
       "1        2                      Jumanji (1995)  Adventure|Children's|Fantasy\n",
       "2        3             Grumpier Old Men (1995)                Comedy|Romance\n",
       "3        4            Waiting to Exhale (1995)                  Comedy|Drama\n",
       "4        5  Father of the Bride Part II (1995)                        Comedy"
      ]
     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "# loading the datasets \n",
    "movies_data=pd.read_csv('Downloads/movies.dat', sep='::',engine='python',encoding='latin-1',names=['Movieid','Title','Genres'])\n",
    "movies_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ca1236eb",
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       "(3883, 3)"
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    "movies_data.shape"
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       "      <td>1</td>\n",
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       "   Userid Gender  Age  Occupation Zipcode\n",
       "0       1      F    1          10   48067\n",
       "1       2      M   56          16   70072\n",
       "2       3      M   25          15   55117\n",
       "3       4      M   45           7   02460\n",
       "4       5      M   25          20   55455"
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   "source": [
    "users_data=pd.read_csv('Downloads/users.dat', sep='::', engine='python', encoding='latin-1',names=['Userid','Gender','Age','Occupation','Zipcode'])\n",
    "users_data.head()"
   ]
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    "users_data.shape"
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       "      <td>978824291</td>\n",
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       "   Userid  Movieid  Ratings  Timestamp\n",
       "0       1     1193        5  978300760\n",
       "1       1      661        3  978302109\n",
       "2       1      914        3  978301968\n",
       "3       1     3408        4  978300275\n",
       "4       1     2355        5  978824291"
      ]
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     "execution_count": 6,
     "metadata": {},
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   ],
   "source": [
    "ratings_data=pd.read_csv('Downloads/ratings.dat', sep='::',engine='python',encoding='latin-1',names=['Userid','Movieid','Ratings','Timestamp'])\n",
    "ratings_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "589f0c27",
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   "outputs": [
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       "(1000209, 4)"
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     "execution_count": 7,
     "metadata": {},
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   "source": [
    "ratings_data.shape"
   ]
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  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca51ca89",
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   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movieid</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
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       "      <th>Ratings</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
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       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>978225952</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>978226474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Movieid             Title                       Genres  Userid  Ratings  \\\n",
       "0        1  Toy Story (1995)  Animation|Children's|Comedy       1        5   \n",
       "1        1  Toy Story (1995)  Animation|Children's|Comedy       6        4   \n",
       "2        1  Toy Story (1995)  Animation|Children's|Comedy       8        4   \n",
       "3        1  Toy Story (1995)  Animation|Children's|Comedy       9        5   \n",
       "4        1  Toy Story (1995)  Animation|Children's|Comedy      10        5   \n",
       "\n",
       "   Timestamp  \n",
       "0  978824268  \n",
       "1  978237008  \n",
       "2  978233496  \n",
       "3  978225952  \n",
       "4  978226474  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging movie data with respect to ratings data\n",
    "\n",
    "ratings_data=movies_data.merge(ratings_data,on='Movieid',how='inner')\n",
    "ratings_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9983d091",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Movieid</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Userid</th>\n",
       "      <th>Ratings</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
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       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824351</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
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       "      <td>48067</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978301777</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>978300760</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824195</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Movieid                                      Title  \\\n",
       "0        1                           Toy Story (1995)   \n",
       "1       48                          Pocahontas (1995)   \n",
       "2      150                           Apollo 13 (1995)   \n",
       "3      260  Star Wars: Episode IV - A New Hope (1977)   \n",
       "4      527                    Schindler's List (1993)   \n",
       "\n",
       "                                 Genres  Userid  Ratings  Timestamp Gender  \\\n",
       "0           Animation|Children's|Comedy       1        5  978824268      F   \n",
       "1  Animation|Children's|Musical|Romance       1        5  978824351      F   \n",
       "2                                 Drama       1        5  978301777      F   \n",
       "3       Action|Adventure|Fantasy|Sci-Fi       1        4  978300760      F   \n",
       "4                             Drama|War       1        5  978824195      F   \n",
       "\n",
       "   Age  Occupation Zipcode  \n",
       "0    1          10   48067  \n",
       "1    1          10   48067  \n",
       "2    1          10   48067  \n",
       "3    1          10   48067  \n",
       "4    1          10   48067  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merging the ratings data with respect to users data and creating new data set\n",
    "\n",
    "main_data=ratings_data.merge(users_data,on='Userid',how='inner')\n",
    "main_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f265db1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 10)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22003e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Movieid       0\n",
       "Title         0\n",
       "Genres        0\n",
       "Userid        0\n",
       "Ratings       0\n",
       "Timestamp     0\n",
       "Gender        0\n",
       "Age           0\n",
       "Occupation    0\n",
       "Zipcode       0\n",
       "dtype: int64"
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "main_data.isnull().sum() # checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eb9888aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000209 entries, 0 to 1000208\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count    Dtype \n",
      "---  ------      --------------    ----- \n",
      " 0   Movieid     1000209 non-null  int64 \n",
      " 1   Title       1000209 non-null  object\n",
      " 2   Genres      1000209 non-null  object\n",
      " 3   Userid      1000209 non-null  int64 \n",
      " 4   Ratings     1000209 non-null  int64 \n",
      " 5   Timestamp   1000209 non-null  int64 \n",
      " 6   Gender      1000209 non-null  object\n",
      " 7   Age         1000209 non-null  int64 \n",
      " 8   Occupation  1000209 non-null  int64 \n",
      " 9   Zipcode     1000209 non-null  object\n",
      "dtypes: int64(6), object(4)\n",
      "memory usage: 83.9+ MB\n"
     ]
    }
   ],
   "source": [
    "main_data.info() # checking the information about the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fdeda06d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25    395556\n",
       "35    199003\n",
       "18    183536\n",
       "45     83633\n",
       "50     72490\n",
       "56     38780\n",
       "1      27211\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data['Age'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9dd8e0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual representation \n",
    "\n",
    "main_data['Age'].value_counts().plot(kind='bar')\n",
    "plt.title('User age distribution')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('users acount')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c0c34198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Movieid</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Userid</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>979168267</td>\n",
       "      <td>F</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>95370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>771</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>978152541</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>95825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>978461604</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>90049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2779</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>1004410663</td>\n",
       "      <td>M</td>\n",
       "      <td>45</td>\n",
       "      <td>17</td>\n",
       "      <td>98052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3269</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>978064964</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>92107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630524</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>5956</td>\n",
       "      <td>2</td>\n",
       "      <td>957079525</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>02142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630817</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>5961</td>\n",
       "      <td>4</td>\n",
       "      <td>957022911</td>\n",
       "      <td>F</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>78757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631125</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>6001</td>\n",
       "      <td>3</td>\n",
       "      <td>956805460</td>\n",
       "      <td>F</td>\n",
       "      <td>25</td>\n",
       "      <td>7</td>\n",
       "      <td>94117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631359</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>6019</td>\n",
       "      <td>4</td>\n",
       "      <td>956761170</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>10024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631448</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children's|Fantasy</td>\n",
       "      <td>6030</td>\n",
       "      <td>4</td>\n",
       "      <td>956719238</td>\n",
       "      <td>M</td>\n",
       "      <td>25</td>\n",
       "      <td>17</td>\n",
       "      <td>32618</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>701 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Movieid           Title                        Genres  Userid  \\\n",
       "370           2  Jumanji (1995)  Adventure|Children's|Fantasy      10   \n",
       "771           2  Jumanji (1995)  Adventure|Children's|Fantasy      18   \n",
       "1353          2  Jumanji (1995)  Adventure|Children's|Fantasy      23   \n",
       "2779          2  Jumanji (1995)  Adventure|Children's|Fantasy      44   \n",
       "3269          2  Jumanji (1995)  Adventure|Children's|Fantasy      48   \n",
       "...         ...             ...                           ...     ...   \n",
       "630524        2  Jumanji (1995)  Adventure|Children's|Fantasy    5956   \n",
       "630817        2  Jumanji (1995)  Adventure|Children's|Fantasy    5961   \n",
       "631125        2  Jumanji (1995)  Adventure|Children's|Fantasy    6001   \n",
       "631359        2  Jumanji (1995)  Adventure|Children's|Fantasy    6019   \n",
       "631448        2  Jumanji (1995)  Adventure|Children's|Fantasy    6030   \n",
       "\n",
       "        Ratings   Timestamp Gender  Age  Occupation Zipcode  \n",
       "370           5   979168267      F   35           1   95370  \n",
       "771           2   978152541      F   18           3   95825  \n",
       "1353          2   978461604      M   35           0   90049  \n",
       "2779          4  1004410663      M   45          17   98052  \n",
       "3269          3   978064964      M   25           4   92107  \n",
       "...         ...         ...    ...  ...         ...     ...  \n",
       "630524        2   957079525      F   18           4   02142  \n",
       "630817        4   957022911      F   45           0   78757  \n",
       "631125        3   956805460      F   25           7   94117  \n",
       "631359        4   956761170      M   25           0   10024  \n",
       "631448        4   956719238      M   25          17   32618  \n",
       "\n",
       "[701 rows x 10 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# user ratings for a movies \n",
    "\n",
    "movie_rating=main_data[main_data['Title'].str.contains('Jumanji')]\n",
    "movie_rating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47c4b3dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visual representation\n",
    "\n",
    "movie_rating.groupby(['Title','Ratings']).size().unstack().plot(kind='bar',stacked=False, legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "24fef13c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "movie_rating.groupby(['Age','Ratings']).size().unstack().plot(kind='bar',stacked=False, legend=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a260bce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Movieid</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Userid</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824351</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978301777</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>978300760</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824195</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Movieid                                      Title  \\\n",
       "0        1                           Toy Story (1995)   \n",
       "1       48                          Pocahontas (1995)   \n",
       "2      150                           Apollo 13 (1995)   \n",
       "3      260  Star Wars: Episode IV - A New Hope (1977)   \n",
       "4      527                    Schindler's List (1993)   \n",
       "\n",
       "                                 Genres  Userid  Ratings  Timestamp Gender  \\\n",
       "0           Animation|Children's|Comedy       1        5  978824268      F   \n",
       "1  Animation|Children's|Musical|Romance       1        5  978824351      F   \n",
       "2                                 Drama       1        5  978301777      F   \n",
       "3       Action|Adventure|Fantasy|Sci-Fi       1        4  978300760      F   \n",
       "4                             Drama|War       1        5  978824195      F   \n",
       "\n",
       "   Age  Occupation Zipcode  \n",
       "0    1          10   48067  \n",
       "1    1          10   48067  \n",
       "2    1          10   48067  \n",
       "3    1          10   48067  \n",
       "4    1          10   48067  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af0d8b7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "main_data.to_csv('main_data.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4bf961c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Movieid</th>\n",
       "      <th>Title</th>\n",
       "      <th>Genres</th>\n",
       "      <th>Userid</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Animation|Children's|Comedy</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824268</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>Pocahontas (1995)</td>\n",
       "      <td>Animation|Children's|Musical|Romance</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824351</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150</td>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978301777</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260</td>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>Action|Adventure|Fantasy|Sci-Fi</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>978300760</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>527</td>\n",
       "      <td>Schindler's List (1993)</td>\n",
       "      <td>Drama|War</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>978824195</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>48067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Movieid                                      Title  \\\n",
       "0        1                           Toy Story (1995)   \n",
       "1       48                          Pocahontas (1995)   \n",
       "2      150                           Apollo 13 (1995)   \n",
       "3      260  Star Wars: Episode IV - A New Hope (1977)   \n",
       "4      527                    Schindler's List (1993)   \n",
       "\n",
       "                                 Genres  Userid  Ratings  Timestamp Gender  \\\n",
       "0           Animation|Children's|Comedy       1        5  978824268      F   \n",
       "1  Animation|Children's|Musical|Romance       1        5  978824351      F   \n",
       "2                                 Drama       1        5  978301777      F   \n",
       "3       Action|Adventure|Fantasy|Sci-Fi       1        4  978300760      F   \n",
       "4                             Drama|War       1        5  978824195      F   \n",
       "\n",
       "   Age  Occupation Zipcode  \n",
       "0    1          10   48067  \n",
       "1    1          10   48067  \n",
       "2    1          10   48067  \n",
       "3    1          10   48067  \n",
       "4    1          10   48067  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('main_data.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "25542189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000209, 10)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd70f6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the data into features and Targets\n",
    "\n",
    "X=df.drop(columns=['Title','Genres','Timestamp','Gender','Zipcode','Ratings'],axis=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "175deac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y=df['Ratings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dda49697",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Movieid  Userid  Age  Occupation\n",
      "0              1       1    1          10\n",
      "1             48       1    1          10\n",
      "2            150       1    1          10\n",
      "3            260       1    1          10\n",
      "4            527       1    1          10\n",
      "...          ...     ...  ...         ...\n",
      "1000204     3513    5727   25           4\n",
      "1000205     3535    5727   25           4\n",
      "1000206     3536    5727   25           4\n",
      "1000207     3555    5727   25           4\n",
      "1000208     3578    5727   25           4\n",
      "\n",
      "[1000209 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a2aafffc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0          5\n",
      "1          5\n",
      "2          5\n",
      "3          4\n",
      "4          5\n",
      "          ..\n",
      "1000204    4\n",
      "1000205    2\n",
      "1000206    5\n",
      "1000207    3\n",
      "1000208    5\n",
      "Name: Ratings, Length: 1000209, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b1d07716",
   "metadata": {},
   "outputs": [],
   "source": [
    "# spliting the data into test and training sets\n",
    "\n",
    "X_train, X_test, Y_train, Y_test=train_test_split(X, Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58e9579a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000209, 4) (800167, 4) (200042, 4)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape,X_train.shape,X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "713e39bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Logistic Regression model training \n",
    "\n",
    "model=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "11387147",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\anaconda\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the model \n",
    "\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc504626",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1bbdc0c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy on trainig data \n",
    "X_train_prediction=model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1f73cbb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 4 4 ... 4 4 4]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e8b15285",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training data : 0.348812185456286\n"
     ]
    }
   ],
   "source": [
    "training_data_accuracy=accuracy_score(Y_train,X_train_prediction)\n",
    "\n",
    "print('Accuracy on training data :', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a9bb8fca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy on test data \n",
    "Y_test_prediction=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2695671f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 4 4 ... 4 4 4]\n"
     ]
    }
   ],
   "source": [
    "print(Y_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "582b2ca2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on testing data : 0.34890672958678676\n"
     ]
    }
   ],
   "source": [
    "testing_data_accuracy = accuracy_score(Y_test, Y_test_prediction)\n",
    "\n",
    "print('Accuracy on testing data :', testing_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d638f8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using Random ForestRegressor model\n",
    "\n",
    "model_2=RandomForestRegressor(n_estimators=100)\n",
    "model_2.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ae01263d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_test_prediction=model_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "201be29f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8785862506125471"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.score(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "591dc311",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_random_forest = round(model_2.score(X_train, Y_train) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9fb45bb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87.86"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_random_forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cf2ad22",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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