Jupyter_Notebook_Files/ML_Dataframe_and_Model_Creation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa9d109e",
   "metadata": {},
   "source": [
    "# Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0b36a914",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "from collections import Counter\n",
    "import sqlalchemy as db\n",
    "from sqlalchemy import create_engine\n",
    "import io\n",
    "import psycopg2\n",
    "\n",
    "# Import Data Preparation Dependency for Algorithms\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Import Tree Regressor\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec93d9a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import db_password, username"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1cdf9be",
   "metadata": {},
   "source": [
    "# Connect To PostgreSQL Database With SQLAlchemy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cb14e694",
   "metadata": {},
   "outputs": [],
   "source": [
    "host = 'movies-fp.cpige012zhtw.us-east-1.rds.amazonaws.com'\n",
    "port = 5432\n",
    "passw = db_password\n",
    "database = \"postgres\"\n",
    "port=5432\n",
    "\n",
    "   \n",
    "db_string = \"postgresql://\" + username + \":\" + passw + \"@\" + host + \":\" + \"5432/\" + database\n",
    "engine = db.create_engine(db_string)\n",
    "connection = engine.connect()\n",
    "metadata = db.MetaData()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091f0c4c",
   "metadata": {},
   "source": [
    "# Query Database and Select Tables To Import and Create Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "228dc2f6",
   "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>movie_id</th>\n",
       "      <th>mg_id</th>\n",
       "      <th>genre_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  mg_id  genre_id\n",
       "0         1      0         1\n",
       "1         1      1         3\n",
       "2         1      2         4\n",
       "3         1      3         5\n",
       "4         1      4         9"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_genres = db.Table('movie_genre', metadata, autoload=True, autoload_with=engine)\n",
    "query = db.select([movie_genres]) \n",
    "ResultProxy = connection.execute(query)\n",
    "ResultSet = ResultProxy.fetchall()\n",
    "mov_gen_df = pd.DataFrame(ResultSet)\n",
    "mov_gen_df.columns = ResultSet[0].keys()\n",
    "mov_gen_df = mov_gen_df.loc[:, ['movie_id', 'mg_id', 'genre_id']]\n",
    "\n",
    "mov_gen_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbcf31bc",
   "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>movie_id</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji</td>\n",
       "      <td>1995</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Grumpier Old Men</td>\n",
       "      <td>1995</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Waiting to Exhale</td>\n",
       "      <td>1995</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Father of the Bride Part II</td>\n",
       "      <td>1995</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id                         name  year  runtime\n",
       "0         1                    Toy Story  1995       81\n",
       "1         2                      Jumanji  1995      104\n",
       "2         3             Grumpier Old Men  1995      101\n",
       "3         4            Waiting to Exhale  1995      127\n",
       "4         5  Father of the Bride Part II  1995      106"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies = db.Table('movies', metadata, autoload=True, autoload_with=engine)\n",
    "query = db.select([movies]) \n",
    "ResultProxy = connection.execute(query)\n",
    "ResultSet = ResultProxy.fetchall()\n",
    "movies_df = pd.DataFrame(ResultSet)\n",
    "movies_df.columns = ResultSet[0].keys()\n",
    "movies_df = movies_df.loc[:, [\"movie_id\", \"name\", \"year\", \"runtime\"]]\n",
    "\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "271d8655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>movie_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2076</td>\n",
       "      <td>47452</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2077</td>\n",
       "      <td>47452</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2078</td>\n",
       "      <td>47452</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2080</td>\n",
       "      <td>47452</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2081</td>\n",
       "      <td>47452</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  user_id  rating\n",
       "0      2076    47452     4.0\n",
       "1      2077    47452     2.0\n",
       "2      2078    47452     3.0\n",
       "3      2080    47452     3.0\n",
       "4      2081    47452     4.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings = db.Table('ratings', metadata, autoload=True, autoload_with=engine)\n",
    "query = db.select([ratings]) \n",
    "ResultProxy = connection.execute(query)\n",
    "ResultSet = ResultProxy.fetchall()\n",
    "ratings_df = pd.DataFrame(ResultSet)\n",
    "ratings_df.columns = ResultSet[0].keys()\n",
    "ratings_df = ratings_df.loc[:, ['movie_id', 'user_id', 'rating']]\n",
    "\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c10b8881",
   "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>movie_id</th>\n",
       "      <th>cast_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>67773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  cast_id\n",
       "0         1       31\n",
       "1         2     2157\n",
       "2         3     6837\n",
       "3         4     8851\n",
       "4         5    67773"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cast = db.Table('movie_cast', metadata, autoload=True, autoload_with=engine)\n",
    "query = db.select([cast])\n",
    "ResultProxy = connection.execute(query)\n",
    "ResultSet = ResultProxy.fetchall()\n",
    "cast_df = pd.DataFrame(ResultSet)\n",
    "cast_df.columns = ResultSet[0].keys()\n",
    "cast_df = cast_df.loc[:, ['movie_id', 'cast_id']]\n",
    "\n",
    "cast_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "be6982a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "connection.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f5592b01",
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_no_genres(column):\n",
    "    i = -1\n",
    "    na = float(\"Nan\")\n",
    "    mov_gen_df[\"clean_genres\"] = \"\"\n",
    "    for row in column:\n",
    "        i += 1\n",
    "        if row == 20:\n",
    "            mov_gen_df.at[i, \"clean_genres\"] = na\n",
    "        else:\n",
    "            mov_gen_df.at[i, \"clean_genres\"] = row\n",
    "    return mov_gen_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1640aae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>movie_id</th>\n",
       "      <th>mg_id</th>\n",
       "      <th>genre_id</th>\n",
       "      <th>clean_genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  mg_id  genre_id clean_genres\n",
       "0         1      0         1            1\n",
       "1         1      1         3            3\n",
       "2         1      2         4            4\n",
       "3         1      3         5            5\n",
       "4         1      4         9            9"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_no_genres(mov_gen_df[\"genre_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0338daf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id        106104\n",
       "mg_id           106104\n",
       "genre_id        106104\n",
       "clean_genres    101838\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov_gen_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb8743d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mov_gen_df = mov_gen_df.drop_duplicates(keep=\"first\")\n",
    "mov_gen_df = mov_gen_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "797c7d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id        101838\n",
       "mg_id           101838\n",
       "genre_id        101838\n",
       "clean_genres    101838\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov_gen_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3e125a81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>mg_id</th>\n",
       "      <th>genre_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id  mg_id  genre_id\n",
       "0         1      0         1\n",
       "1         1      1         3\n",
       "2         1      2         4\n",
       "3         1      3         5\n",
       "4         1      4         9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mov_gen_df = mov_gen_df.drop(columns=[\"clean_genres\"])\n",
    "mov_gen_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86b44933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Index to Common Column of movie_id For Easier Join\n",
    "movies_df = movies_df.set_index(\"movie_id\")\n",
    "ratings_df = ratings_df.set_index(\"movie_id\")\n",
    "mov_gen_df = mov_gen_df.set_index(\"movie_id\")\n",
    "cast_df = cast_df.set_index(\"movie_id\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1390ad12",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  rating       name    year  runtime  mg_id  genre_id  \\\n",
       "movie_id                                                                 \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    0.0       1.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    1.0       3.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    2.0       4.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    3.0       5.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    4.0       9.0   \n",
       "\n",
       "          cast_id  \n",
       "movie_id           \n",
       "1            31.0  \n",
       "1            31.0  \n",
       "1            31.0  \n",
       "1            31.0  \n",
       "1            31.0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = ratings_df.join(movies_df, on=\"movie_id\", how=\"left\", sort=True)\n",
    "data_df = data_df.join(mov_gen_df, on=\"movie_id\", how=\"left\", sort=True)\n",
    "data_df = data_df.join(cast_df, on=\"movie_id\", how=\"left\", sort=True)\n",
    "\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2baa9ade",
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id  rating       name    year  runtime  mg_id  genre_id  \\\n",
       "movie_id                                                                 \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    0.0       1.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    1.0       3.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    2.0       4.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    3.0       5.0   \n",
       "1           47453     4.0  Toy Story  1995.0     81.0    4.0       9.0   \n",
       "\n",
       "          cast_id  movie_id  \n",
       "movie_id                     \n",
       "1            31.0         1  \n",
       "1            31.0         1  \n",
       "1            31.0         1  \n",
       "1            31.0         1  \n",
       "1            31.0         1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df[\"movie_id\"] = data_df.index\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ee8fd43d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Toy Story</td>\n",
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       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  rating       name    year  runtime  mg_id  genre_id  cast_id  \\\n",
       "0    47453     4.0  Toy Story  1995.0     81.0    0.0       1.0     31.0   \n",
       "1    47453     4.0  Toy Story  1995.0     81.0    1.0       3.0     31.0   \n",
       "2    47453     4.0  Toy Story  1995.0     81.0    2.0       4.0     31.0   \n",
       "3    47453     4.0  Toy Story  1995.0     81.0    3.0       5.0     31.0   \n",
       "4    47453     4.0  Toy Story  1995.0     81.0    4.0       9.0     31.0   \n",
       "\n",
       "   movie_id  \n",
       "0         1  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = data_df.reset_index(drop=True)\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e9a5ccfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id     53797871\n",
       "rating      53797871\n",
       "name        53638017\n",
       "year        53638017\n",
       "runtime     53638017\n",
       "mg_id       49759845\n",
       "genre_id    49759845\n",
       "cast_id     53261317\n",
       "movie_id    53797871\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "aa14ff29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.363802e+07\n",
       "mean     1.152789e+02\n",
       "std      2.510561e+01\n",
       "min      0.000000e+00\n",
       "25%      9.900000e+01\n",
       "50%      1.120000e+02\n",
       "75%      1.270000e+02\n",
       "max      1.256000e+03\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df[\"runtime\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "634c24e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     53889.000000\n",
       "mean        998.308950\n",
       "std        7198.073055\n",
       "min           1.000000\n",
       "25%           2.000000\n",
       "50%           7.000000\n",
       "75%          60.000000\n",
       "max      342345.000000\n",
       "Name: movie_id, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = data_df[\"movie_id\"].value_counts()\n",
    "counts.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0edc7f41",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1         342345\n",
      "589       321290\n",
      "1198      317525\n",
      "590       311262\n",
      "356       291120\n",
      "           ...  \n",
      "135466         1\n",
      "173471         1\n",
      "135416         1\n",
      "135414         1\n",
      "176747         1\n",
      "Name: movie_id, Length: 53889, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(data_df[\"movie_id\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "29ff1260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>31170</td>\n",
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       "      <th>4</th>\n",
       "      <td>8967</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>15474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   movie_id\n",
       "1    342345\n",
       "2     81429\n",
       "3     31170\n",
       "4      8967\n",
       "5     15474"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_ids = data_df[\"movie_id\"].unique()\n",
    "\n",
    "movie_counts = pd.DataFrame(data=counts, index=movie_ids)\n",
    "movie_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e3c2e4ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Bigge\\.conda\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "C:\\Users\\Bigge\\.conda\\envs\\mlenv\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "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>movie_id</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>342345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>81429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>31170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>15474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4859</th>\n",
       "      <td>177615</td>\n",
       "      <td>1464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>177765</td>\n",
       "      <td>2701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>179819</td>\n",
       "      <td>2273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4862</th>\n",
       "      <td>180031</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4863</th>\n",
       "      <td>187593</td>\n",
       "      <td>1633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4864 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      movie_id  counts\n",
       "0            1  342345\n",
       "1            2   81429\n",
       "2            3   31170\n",
       "3            4    8967\n",
       "4            5   15474\n",
       "...        ...     ...\n",
       "4859    177615    1464\n",
       "4860    177765    2701\n",
       "4861    179819    2273\n",
       "4862    180031    1473\n",
       "4863    187593    1633\n",
       "\n",
       "[4864 rows x 2 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_movie_ids = movie_counts.loc[movie_counts[\"movie_id\"] > 1000]\n",
    "clean_movie_ids[\"counts\"] = clean_movie_ids[\"movie_id\"]\n",
    "clean_movie_ids[\"movie_id\"] = clean_movie_ids.index\n",
    "clean_movie_ids = clean_movie_ids.reset_index(drop=True)\n",
    "clean_movie_ids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "06fd9d4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>counts</th>\n",
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       "  </thead>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>8967</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   movie_id  counts\n",
       "0         1  342345\n",
       "1         2   81429\n",
       "2         3   31170\n",
       "3         4    8967\n",
       "4         5   15474"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_movie_ids.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "07261329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4864"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clean_movie_ids[\"movie_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "88553506",
   "metadata": {},
   "outputs": [],
   "source": [
    "na = float(\"Nan\")\n",
    "data_df[\"new_movie_id\"] = \"\"\n",
    "\n",
    "def limit_ratings(column, column_2):\n",
    "    i = -1\n",
    "    for entry in column:\n",
    "        i += 1\n",
    "        if entry in column_2:\n",
    "            data_df.at[i, \"new_movie_id\"] = entry\n",
    "        else:\n",
    "            data_df.at[i, \"new_movie_id\"] = na\n",
    "    \n",
    "    return data_df.head()\n",
    "            \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6fcfb691",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>rating</th>\n",
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       "      <th>year</th>\n",
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       "      <th>mg_id</th>\n",
       "      <th>genre_id</th>\n",
       "      <th>cast_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>new_movie_id</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  rating       name    year  runtime  mg_id  genre_id  cast_id  \\\n",
       "0    47453     4.0  Toy Story  1995.0     81.0    0.0       1.0     31.0   \n",
       "1    47453     4.0  Toy Story  1995.0     81.0    1.0       3.0     31.0   \n",
       "2    47453     4.0  Toy Story  1995.0     81.0    2.0       4.0     31.0   \n",
       "3    47453     4.0  Toy Story  1995.0     81.0    3.0       5.0     31.0   \n",
       "4    47453     4.0  Toy Story  1995.0     81.0    4.0       9.0     31.0   \n",
       "\n",
       "   movie_id new_movie_id  \n",
       "0         1            1  \n",
       "1         1            1  \n",
       "2         1            1  \n",
       "3         1            1  \n",
       "4         1            1  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "limit_ratings(data_df[\"movie_id\"], clean_movie_ids[\"movie_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6599c389",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         53797871\n",
       "rating          53797871\n",
       "name            53638017\n",
       "year            53638017\n",
       "runtime         53638017\n",
       "mg_id           49759845\n",
       "genre_id        49759845\n",
       "cast_id         53261317\n",
       "movie_id        53797871\n",
       "new_movie_id    38846840\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "98e3ce91",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = data_df.drop_duplicates(keep=\"first\")\n",
    "data_df = data_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9ecd8852",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id         38635675\n",
       "rating          38635675\n",
       "name            38635675\n",
       "year            38635675\n",
       "runtime         38635675\n",
       "mg_id           38635675\n",
       "genre_id        38635675\n",
       "cast_id         38635675\n",
       "movie_id        38635675\n",
       "new_movie_id    38635675\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "afa53f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>new_movie_id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>47453</td>\n",
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       "      <td>1995</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  rating       name  year  runtime  mg_id  genre_id  cast_id  \\\n",
       "0    47453     4.0  Toy Story  1995     81.0    0.0         1       31   \n",
       "1    47453     4.0  Toy Story  1995     81.0    1.0         3       31   \n",
       "2    47453     4.0  Toy Story  1995     81.0    2.0         4       31   \n",
       "3    47453     4.0  Toy Story  1995     81.0    3.0         5       31   \n",
       "4    47453     4.0  Toy Story  1995     81.0    4.0         9       31   \n",
       "\n",
       "   movie_id new_movie_id  \n",
       "0         1            1  \n",
       "1         1            1  \n",
       "2         1            1  \n",
       "3         1            1  \n",
       "4         1            1  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df[\"year\"] = data_df[\"year\"].astype(int)\n",
    "# data_df[\"mg_id\"] = data_df[\"mg_id\"].astype(int)\n",
    "data_df[\"genre_id\"] = data_df[\"genre_id\"].astype(int)\n",
    "data_df[\"cast_id\"] = data_df[\"cast_id\"].astype(int)\n",
    "\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ecd4c3bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df[\"trimmed_runtime\"] = data_df[\"runtime\"].loc[data_df[\"runtime\"] > 59]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "76cfc939",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id            38635675\n",
       "rating             38635675\n",
       "name               38635675\n",
       "year               38635675\n",
       "runtime            38635675\n",
       "mg_id              38635675\n",
       "genre_id           38635675\n",
       "cast_id            38635675\n",
       "movie_id           38635675\n",
       "new_movie_id       38635675\n",
       "trimmed_runtime    38549840\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9ba02c5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df = data_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7190d96a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id            38549840\n",
       "rating             38549840\n",
       "name               38549840\n",
       "year               38549840\n",
       "runtime            38549840\n",
       "mg_id              38549840\n",
       "genre_id           38549840\n",
       "cast_id            38549840\n",
       "movie_id           38549840\n",
       "new_movie_id       38549840\n",
       "trimmed_runtime    38549840\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dacbb3e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>mg_id</th>\n",
       "      <th>genre_id</th>\n",
       "      <th>cast_id</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  rating       name  year  runtime  mg_id  genre_id  cast_id  \\\n",
       "0    47453     4.0  Toy Story  1995     81.0    0.0         1       31   \n",
       "1    47453     4.0  Toy Story  1995     81.0    1.0         3       31   \n",
       "2    47453     4.0  Toy Story  1995     81.0    2.0         4       31   \n",
       "3    47453     4.0  Toy Story  1995     81.0    3.0         5       31   \n",
       "4    47453     4.0  Toy Story  1995     81.0    4.0         9       31   \n",
       "\n",
       "   movie_id  \n",
       "0         1  \n",
       "1         1  \n",
       "2         1  \n",
       "3         1  \n",
       "4         1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df = data_df.drop(columns=[\"new_movie_id\", \"trimmed_runtime\"])\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7b4a9b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_df.to_csv(\"ML_Dataframe.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736f8976",
   "metadata": {},
   "source": [
    "# Machine Learning Model Creation and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6bb8a512",
   "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>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genre_id</th>\n",
       "      <th>cast_id</th>\n",
       "      <th>movie_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  runtime  genre_id  cast_id  movie_id\n",
       "0  1995     81.0         1       31         1\n",
       "1  1995     81.0         3       31         1\n",
       "2  1995     81.0         4       31         1\n",
       "3  1995     81.0         5       31         1\n",
       "4  1995     81.0         9       31         1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Declare X and y Variable Values From Dataframe\n",
    "X = data_df.drop(columns=[\"user_id\", \"rating\", \"name\", \"mg_id\"])\n",
    "y = data_df[\"rating\"]\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "edf9e579",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create Training and Testing Sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "754424b6",
   "metadata": {},
   "source": [
    "# Decision Tree Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c7ea5e41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(random_state=0)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and Fit Model With Features\n",
    "model = DecisionTreeRegressor(random_state=0)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c5f83960",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1896050371860699"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Determine Coefficient of Determination (R^2)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0da9237b",
   "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>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>name</th>\n",
       "      <th>year</th>\n",
       "      <th>runtime</th>\n",
       "      <th>mg_id</th>\n",
       "      <th>genre_id</th>\n",
       "      <th>cast_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>Predicted Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.886184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.884246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.884522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.889121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>47453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>1995</td>\n",
       "      <td>81.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>3.887248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  rating       name  year  runtime  mg_id  genre_id  cast_id  \\\n",
       "0    47453     4.0  Toy Story  1995     81.0    0.0         1       31   \n",
       "1    47453     4.0  Toy Story  1995     81.0    1.0         3       31   \n",
       "2    47453     4.0  Toy Story  1995     81.0    2.0         4       31   \n",
       "3    47453     4.0  Toy Story  1995     81.0    3.0         5       31   \n",
       "4    47453     4.0  Toy Story  1995     81.0    4.0         9       31   \n",
       "\n",
       "   movie_id  Predicted Rating  \n",
       "0         1          3.886184  \n",
       "1         1          3.884246  \n",
       "2         1          3.884522  \n",
       "3         1          3.889121  \n",
       "4         1          3.887248  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predict Rating Values Based on Initial Datatable Features, Populate Dataframe Column\n",
    "dcsntree_preds = model.predict(X)\n",
    "data_df[\"Predicted Rating\"] = dcsntree_preds\n",
    "data_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea88179",
   "metadata": {},
   "source": [
    "# Export to CSV File For Visualization Software Access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "106ebce5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_df.to_csv(\"full_df_prediction_dataframe.csv\", index=False)"
   ]
  }
 ],
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