data_etl.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy import inspect\n",
    "import config\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract CSVs into Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|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|Romance</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  \\\n",
       "0        1                    Toy Story (1995)   \n",
       "1        2                      Jumanji (1995)   \n",
       "2        3             Grumpier Old Men (1995)   \n",
       "3        4            Waiting to Exhale (1995)   \n",
       "4        5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_file = \"Resources/movies.csv\"\n",
    "movies_df = pd.read_csv(movies_file)\n",
    "movies_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userId</th>\n",
       "      <th>movieId</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   userId  movieId  rating  timestamp\n",
       "0       1        1     4.0  964982703\n",
       "1       1        3     4.0  964981247\n",
       "2       1        6     4.0  964982224\n",
       "3       1       47     5.0  964983815\n",
       "4       1       50     5.0  964982931"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_file = \"Resources/ratings.csv\"\n",
    "ratings_df = pd.read_csv(ratings_file)\n",
    "ratings_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform movies Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|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|Romance</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": [
       "   movie_id                               title  \\\n",
       "0         1                    Toy Story (1995)   \n",
       "1         2                      Jumanji (1995)   \n",
       "2         3             Grumpier Old Men (1995)   \n",
       "3         4            Waiting to Exhale (1995)   \n",
       "4         5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a filtered dataframe from specific columns\n",
    "movies_cols = [\"movieId\",\"title\",\"genres\"]\n",
    "movies_transformed = movies_df[movies_cols].copy()\n",
    "\n",
    "# Rename the column headers\n",
    "movies_transformed= movies_transformed.rename(columns={\n",
    "                                                          \"movieId\": \"movie_id\"\n",
    "                                                          })\n",
    "\n",
    "# Clean the data by dropping duplicates and NA values and setting the index\n",
    "movies_transformed.drop_duplicates(\"movie_id\", inplace=True)\n",
    "movies_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transform ratings Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamps\n",
       "0        1         1     4.0   964982703\n",
       "1        1         3     4.0   964981247\n",
       "2        1         6     4.0   964982224\n",
       "3        1        47     5.0   964983815\n",
       "4        1        50     5.0   964982931"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a filtered dataframe from specific columns\n",
    "ratings_cols = [\"userId\",\"movieId\",\"rating\",\"timestamp\"]\n",
    "ratings_transformed = ratings_df[ratings_cols].copy()\n",
    "\n",
    "# Rename the column headers\n",
    "ratings_transformed= ratings_transformed.rename(columns={\n",
    "                                                          \"movieId\": \"movie_id\",\n",
    "                                                          \"userId\" : \"user_id\",\n",
    "                                                          \"timestamp\" : \"timestamps\"\n",
    "                                                          })\n",
    "\n",
    "# Clean the data by dropping duplicates and NA values and setting the index\n",
    "ratings_transformed.drop_duplicates(subset=[\"user_id\",\"movie_id\"], inplace=True)\n",
    "ratings_transformed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Connect to Local Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "protocol = 'postgresql'\n",
    "username = config.Username\n",
    "password = config.Password\n",
    "host = 'localhost'\n",
    "port = 5432\n",
    "database_name = 'movies_db'\n",
    "rds_connection_string = f'{protocol}://{username}:{password}@{host}:{port}/{database_name}'\n",
    "engine = create_engine(rds_connection_string)\n",
    "insp = inspect(engine)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspect Tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['movies', 'ratings']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insp.get_table_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "742"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_transformed.to_sql(name='movies', con=engine, if_exists='replace', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "836"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratings_transformed.to_sql(name='ratings', con=engine, if_exists='replace', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirm data has been added by querying the movies table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>Adventure|Children|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|Romance</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": [
       "   movie_id                               title  \\\n",
       "0         1                    Toy Story (1995)   \n",
       "1         2                      Jumanji (1995)   \n",
       "2         3             Grumpier Old Men (1995)   \n",
       "3         4            Waiting to Exhale (1995)   \n",
       "4         5  Father of the Bride Part II (1995)   \n",
       "\n",
       "                                        genres  \n",
       "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
       "1                   Adventure|Children|Fantasy  \n",
       "2                               Comedy|Romance  \n",
       "3                         Comedy|Drama|Romance  \n",
       "4                                       Comedy  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from movies', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confirm data has been added by querying the ratings table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964981247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>964982224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964983815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>5.0</td>\n",
       "      <td>964982931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  movie_id  rating  timestamps\n",
       "0        1         1     4.0   964982703\n",
       "1        1         3     4.0   964981247\n",
       "2        1         6     4.0   964982224\n",
       "3        1        47     5.0   964983815\n",
       "4        1        50     5.0   964982931"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_sql_query('select * from ratings', con=engine).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Join Data from movies and ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql_join = r\"\"\"SELECT movies.movie_id, movies.title, movies.genres, ratings.user_id, ratings.rating, ratings.timestamps\n",
    "FROM movies\n",
    "INNER JOIN ratings\n",
    "ON movies.movie_id=ratings.movie_id;\"\"\"\n",
    "data_df=pd.read_sql_query(sql_join, con=engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\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>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamps</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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>214</td>\n",
       "      <td>1</td>\n",
       "      <td>853937855</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>1316196157</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1525548642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>89</td>\n",
       "      <td>1</td>\n",
       "      <td>1520408314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>1524781249</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100831</th>\n",
       "      <td>193581</td>\n",
       "      <td>Black Butler: Book of the Atlantic (2017)</td>\n",
       "      <td>Action|Animation|Comedy|Fantasy</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>1537109082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100832</th>\n",
       "      <td>193583</td>\n",
       "      <td>No Game No Life: Zero (2017)</td>\n",
       "      <td>Animation|Comedy|Fantasy</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>1537109545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100833</th>\n",
       "      <td>193585</td>\n",
       "      <td>Flint (2017)</td>\n",
       "      <td>Drama</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>1537109805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100834</th>\n",
       "      <td>193587</td>\n",
       "      <td>Bungo Stray Dogs: Dead Apple (2018)</td>\n",
       "      <td>Action|Animation</td>\n",
       "      <td>184</td>\n",
       "      <td>1</td>\n",
       "      <td>1537110021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100835</th>\n",
       "      <td>193609</td>\n",
       "      <td>Andrew Dice Clay: Dice Rules (1991)</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>331</td>\n",
       "      <td>1</td>\n",
       "      <td>1537157606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100836 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id                                      title  \\\n",
       "0              1                           Toy Story (1995)   \n",
       "1              1                           Toy Story (1995)   \n",
       "2              1                           Toy Story (1995)   \n",
       "3              1                           Toy Story (1995)   \n",
       "4              1                           Toy Story (1995)   \n",
       "...          ...                                        ...   \n",
       "100831    193581  Black Butler: Book of the Atlantic (2017)   \n",
       "100832    193583               No Game No Life: Zero (2017)   \n",
       "100833    193585                               Flint (2017)   \n",
       "100834    193587        Bungo Stray Dogs: Dead Apple (2018)   \n",
       "100835    193609        Andrew Dice Clay: Dice Rules (1991)   \n",
       "\n",
       "                                             genres  user_id  rating  \\\n",
       "0       Adventure|Animation|Children|Comedy|Fantasy      214       1   \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy      213       1   \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy      153       0   \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy       89       1   \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      233       1   \n",
       "...                                             ...      ...     ...   \n",
       "100831              Action|Animation|Comedy|Fantasy      184       1   \n",
       "100832                     Animation|Comedy|Fantasy      184       1   \n",
       "100833                                        Drama      184       1   \n",
       "100834                             Action|Animation      184       1   \n",
       "100835                                       Comedy      331       1   \n",
       "\n",
       "        timestamps  \n",
       "0        853937855  \n",
       "1       1316196157  \n",
       "2       1525548642  \n",
       "3       1520408314  \n",
       "4       1524781249  \n",
       "...            ...  \n",
       "100831  1537109082  \n",
       "100832  1537109545  \n",
       "100833  1537109805  \n",
       "100834  1537110021  \n",
       "100835  1537157606  \n",
       "\n",
       "[100836 rows x 6 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a cutoff value and create a list of application types to be replaced\n",
    "data_df0=data_df.copy()\n",
    "data_df0['rating'] = np.where(data_df0['rating'] < 3, 0,1)\n",
    "#Check that the transformation worked\n",
    "data_df0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356       329\n",
       "318       317\n",
       "296       307\n",
       "593       279\n",
       "2571      278\n",
       "         ... \n",
       "4093        1\n",
       "4089        1\n",
       "58351       1\n",
       "4083        1\n",
       "193609      1\n",
       "Name: movie_id, Length: 9724, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look at movie value counts for cleanup\n",
    "review_count = data_df['movie_id'].value_counts()\n",
    "review_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "356       329\n",
       "318       317\n",
       "296       307\n",
       "593       279\n",
       "2571      278\n",
       "         ... \n",
       "5329       10\n",
       "5296       10\n",
       "68793      10\n",
       "102481     10\n",
       "45668      10\n",
       "Name: movie_id, Length: 2269, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a cutoff value and create a list of movies to be excluded\n",
    "movies_to_exclude = list(review_count[review_count < 10].index)\n",
    "# Remove from dataframe\n",
    "data_df = data_df[~data_df['movie_id'].isin(movies_to_exclude)]\n",
    "# Check to make sure cleaning was successful\n",
    "data_df['movie_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamps</th>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>Toy Story (1995)</td>\n",
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       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
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       "      <td>4.5</td>\n",
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       "    <tr>\n",
       "      <th>100794</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1535470534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100795</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>98</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1532457913</td>\n",
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       "    <tr>\n",
       "      <th>100796</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>233</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1536968163</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>81116 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id              title  \\\n",
       "0              1   Toy Story (1995)   \n",
       "1              1   Toy Story (1995)   \n",
       "2              1   Toy Story (1995)   \n",
       "3              1   Toy Story (1995)   \n",
       "4              1   Toy Story (1995)   \n",
       "...          ...                ...   \n",
       "100792    187593  Deadpool 2 (2018)   \n",
       "100793    187593  Deadpool 2 (2018)   \n",
       "100794    187593  Deadpool 2 (2018)   \n",
       "100795    187593  Deadpool 2 (2018)   \n",
       "100796    187593  Deadpool 2 (2018)   \n",
       "\n",
       "                                             genres  user_id  rating  \\\n",
       "0       Adventure|Animation|Children|Comedy|Fantasy      214     3.0   \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy      213     3.5   \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy      153     2.0   \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy       89     3.0   \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      233     3.0   \n",
       "...                                             ...      ...     ...   \n",
       "100792                         Action|Comedy|Sci-Fi      249     5.0   \n",
       "100793                         Action|Comedy|Sci-Fi      248     4.5   \n",
       "100794                         Action|Comedy|Sci-Fi       25     5.0   \n",
       "100795                         Action|Comedy|Sci-Fi       98     5.0   \n",
       "100796                         Action|Comedy|Sci-Fi      233     2.5   \n",
       "\n",
       "        timestamps  \n",
       "0        853937855  \n",
       "1       1316196157  \n",
       "2       1525548642  \n",
       "3       1520408314  \n",
       "4       1524781249  \n",
       "...            ...  \n",
       "100792  1531611534  \n",
       "100793  1534599909  \n",
       "100794  1535470534  \n",
       "100795  1532457913  \n",
       "100796  1536968163  \n",
       "\n",
       "[81116 rows x 6 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check remaining ratings\n",
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = data_df0[\"rating\"]\n",
    "X = data_df0.drop([\"rating\",\"title\",\"genres\"],axis=1)\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a StandardScaler() model and fit it to the training data\n",
    "\n",
    "X_scaler = StandardScaler().fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the training and testing data by using the X_scaler and y_scaler models\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 1, Train/Test Score: 1.000/0.754\n",
      "k: 3, Train/Test Score: 0.877/0.788\n",
      "k: 5, Train/Test Score: 0.854/0.800\n",
      "k: 7, Train/Test Score: 0.845/0.806\n",
      "k: 9, Train/Test Score: 0.840/0.808\n",
      "k: 11, Train/Test Score: 0.837/0.810\n",
      "k: 13, Train/Test Score: 0.834/0.812\n",
      "k: 15, Train/Test Score: 0.833/0.812\n",
      "k: 17, Train/Test Score: 0.831/0.811\n",
      "k: 19, Train/Test Score: 0.830/0.813\n"
     ]
    },
    {
     "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": [
    "# Loop through different k values to find which has the highest accuracy.\n",
    "# Note: We use only odd numbers because we don't want any ties.\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for k in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, y_train)\n",
    "    train_score = knn.score(X_train_scaled, y_train)\n",
    "    test_score = knn.score(X_test_scaled, y_test)\n",
    "    train_scores.append(train_score)\n",
    "    test_scores.append(test_score)\n",
    "    print(f\"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}\")\n",
    "    \n",
    "    \n",
    "plt.plot(range(1, 20, 2), train_scores, marker='o')\n",
    "plt.plot(range(1, 20, 2), test_scores, marker=\"x\")\n",
    "plt.xlabel(\"k neighbors\")\n",
    "plt.ylabel(\"Testing accuracy Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=3 Test Acc: 0.788\n"
     ]
    }
   ],
   "source": [
    "# Note that k: 3 provides the best accuracy where the classifier starts to stablize\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "print('k=3 Test Acc: %.3f' % knn.score(X_test_scaled, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Model Optmisation 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>user_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>timestamps</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>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>853937855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>213</td>\n",
       "      <td>0</td>\n",
       "      <td>1316196157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>153</td>\n",
       "      <td>0</td>\n",
       "      <td>1525548642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>89</td>\n",
       "      <td>0</td>\n",
       "      <td>1520408314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1524781249</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100792</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>249</td>\n",
       "      <td>1</td>\n",
       "      <td>1531611534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100793</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>248</td>\n",
       "      <td>0</td>\n",
       "      <td>1534599909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100794</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1535470534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100795</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1532457913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100796</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>233</td>\n",
       "      <td>0</td>\n",
       "      <td>1536968163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81116 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id              title  \\\n",
       "0              1   Toy Story (1995)   \n",
       "1              1   Toy Story (1995)   \n",
       "2              1   Toy Story (1995)   \n",
       "3              1   Toy Story (1995)   \n",
       "4              1   Toy Story (1995)   \n",
       "...          ...                ...   \n",
       "100792    187593  Deadpool 2 (2018)   \n",
       "100793    187593  Deadpool 2 (2018)   \n",
       "100794    187593  Deadpool 2 (2018)   \n",
       "100795    187593  Deadpool 2 (2018)   \n",
       "100796    187593  Deadpool 2 (2018)   \n",
       "\n",
       "                                             genres  user_id  rating  \\\n",
       "0       Adventure|Animation|Children|Comedy|Fantasy      214       0   \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy      213       0   \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy      153       0   \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy       89       0   \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      233       0   \n",
       "...                                             ...      ...     ...   \n",
       "100792                         Action|Comedy|Sci-Fi      249       1   \n",
       "100793                         Action|Comedy|Sci-Fi      248       0   \n",
       "100794                         Action|Comedy|Sci-Fi       25       1   \n",
       "100795                         Action|Comedy|Sci-Fi       98       1   \n",
       "100796                         Action|Comedy|Sci-Fi      233       0   \n",
       "\n",
       "        timestamps  \n",
       "0        853937855  \n",
       "1       1316196157  \n",
       "2       1525548642  \n",
       "3       1520408314  \n",
       "4       1524781249  \n",
       "...            ...  \n",
       "100792  1531611534  \n",
       "100793  1534599909  \n",
       "100794  1535470534  \n",
       "100795  1532457913  \n",
       "100796  1536968163  \n",
       "\n",
       "[81116 rows x 6 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a cutoff value and create a list of application types to be replaced\n",
    "data_df1=data_df.copy()\n",
    "data_df1['rating'] = np.where(data_df1['rating'] < 5, 0,1)\n",
    "#Check that the transformation worked\n",
    "data_df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = data_df1[\"rating\"]\n",
    "X = data_df1.drop([\"rating\",\"title\",\"genres\"],axis=1)\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a StandardScaler() model and fit it to the training data\n",
    "\n",
    "X_scaler = StandardScaler().fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the training and testing data by using the X_scaler and y_scaler models\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 1, Train/Test Score: 1.000/0.803\n",
      "k: 3, Train/Test Score: 0.899/0.835\n",
      "k: 5, Train/Test Score: 0.881/0.844\n",
      "k: 7, Train/Test Score: 0.874/0.849\n",
      "k: 9, Train/Test Score: 0.870/0.851\n",
      "k: 11, Train/Test Score: 0.868/0.852\n",
      "k: 13, Train/Test Score: 0.868/0.854\n",
      "k: 15, Train/Test Score: 0.866/0.855\n",
      "k: 17, Train/Test Score: 0.865/0.855\n",
      "k: 19, Train/Test Score: 0.864/0.854\n"
     ]
    },
    {
     "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": [
    "# Loop through different k values to find which has the highest accuracy.\n",
    "# Note: We use only odd numbers because we don't want any ties.\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for k in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, y_train)\n",
    "    train_score = knn.score(X_train_scaled, y_train)\n",
    "    test_score = knn.score(X_test_scaled, y_test)\n",
    "    train_scores.append(train_score)\n",
    "    test_scores.append(test_score)\n",
    "    print(f\"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}\")\n",
    "    \n",
    "    \n",
    "plt.plot(range(1, 20, 2), train_scores, marker='o')\n",
    "plt.plot(range(1, 20, 2), test_scores, marker=\"x\")\n",
    "plt.xlabel(\"k neighbors\")\n",
    "plt.ylabel(\"Testing accuracy Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=3 Test Acc: 0.835\n"
     ]
    }
   ],
   "source": [
    "# Note that k: 3 provides the best accuracy where the classifier starts to stablize\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "print('k=3 Test Acc: %.3f' % knn.score(X_test_scaled, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Model Optimisation 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>title</th>\n",
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       "      <th>rating</th>\n",
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       "      <td>98</td>\n",
       "      <td>1</td>\n",
       "      <td>1532457913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100796</th>\n",
       "      <td>187593</td>\n",
       "      <td>Deadpool 2 (2018)</td>\n",
       "      <td>Action|Comedy|Sci-Fi</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>1536968163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81116 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id              title  \\\n",
       "0              1   Toy Story (1995)   \n",
       "1              1   Toy Story (1995)   \n",
       "2              1   Toy Story (1995)   \n",
       "3              1   Toy Story (1995)   \n",
       "4              1   Toy Story (1995)   \n",
       "...          ...                ...   \n",
       "100792    187593  Deadpool 2 (2018)   \n",
       "100793    187593  Deadpool 2 (2018)   \n",
       "100794    187593  Deadpool 2 (2018)   \n",
       "100795    187593  Deadpool 2 (2018)   \n",
       "100796    187593  Deadpool 2 (2018)   \n",
       "\n",
       "                                             genres  user_id  rating  \\\n",
       "0       Adventure|Animation|Children|Comedy|Fantasy      214       1   \n",
       "1       Adventure|Animation|Children|Comedy|Fantasy      213       1   \n",
       "2       Adventure|Animation|Children|Comedy|Fantasy      153       0   \n",
       "3       Adventure|Animation|Children|Comedy|Fantasy       89       1   \n",
       "4       Adventure|Animation|Children|Comedy|Fantasy      233       1   \n",
       "...                                             ...      ...     ...   \n",
       "100792                         Action|Comedy|Sci-Fi      249       1   \n",
       "100793                         Action|Comedy|Sci-Fi      248       1   \n",
       "100794                         Action|Comedy|Sci-Fi       25       1   \n",
       "100795                         Action|Comedy|Sci-Fi       98       1   \n",
       "100796                         Action|Comedy|Sci-Fi      233       1   \n",
       "\n",
       "        timestamps  \n",
       "0        853937855  \n",
       "1       1316196157  \n",
       "2       1525548642  \n",
       "3       1520408314  \n",
       "4       1524781249  \n",
       "...            ...  \n",
       "100792  1531611534  \n",
       "100793  1534599909  \n",
       "100794  1535470534  \n",
       "100795  1532457913  \n",
       "100796  1536968163  \n",
       "\n",
       "[81116 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choose a cutoff value and create a list of application types to be replaced\n",
    "data_df2=data_df.copy()\n",
    "data_df2['rating'] = np.where(data_df2['rating'] < 2.5, 0,1)\n",
    "#Check that the transformation worked\n",
    "data_df2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = data_df2[\"rating\"]\n",
    "X = data_df2.drop([\"rating\",\"title\",\"genres\"],axis=1)\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Create a StandardScaler() model and fit it to the training data\n",
    "\n",
    "X_scaler = StandardScaler().fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform the training and testing data by using the X_scaler and y_scaler models\n",
    "\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k: 1, Train/Test Score: 1.000/0.828\n",
      "k: 3, Train/Test Score: 0.913/0.859\n",
      "k: 5, Train/Test Score: 0.899/0.873\n",
      "k: 7, Train/Test Score: 0.893/0.878\n",
      "k: 9, Train/Test Score: 0.891/0.880\n",
      "k: 11, Train/Test Score: 0.889/0.882\n",
      "k: 13, Train/Test Score: 0.888/0.884\n",
      "k: 15, Train/Test Score: 0.888/0.883\n",
      "k: 17, Train/Test Score: 0.887/0.884\n",
      "k: 19, Train/Test Score: 0.887/0.885\n"
     ]
    },
    {
     "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": [
    "# Loop through different k values to find which has the highest accuracy.\n",
    "# Note: We use only odd numbers because we don't want any ties.\n",
    "train_scores = []\n",
    "test_scores = []\n",
    "for k in range(1, 20, 2):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train_scaled, y_train)\n",
    "    train_score = knn.score(X_train_scaled, y_train)\n",
    "    test_score = knn.score(X_test_scaled, y_test)\n",
    "    train_scores.append(train_score)\n",
    "    test_scores.append(test_score)\n",
    "    print(f\"k: {k}, Train/Test Score: {train_score:.3f}/{test_score:.3f}\")\n",
    "    \n",
    "    \n",
    "plt.plot(range(1, 20, 2), train_scores, marker='o')\n",
    "plt.plot(range(1, 20, 2), test_scores, marker=\"x\")\n",
    "plt.xlabel(\"k neighbors\")\n",
    "plt.ylabel(\"Testing accuracy Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=3 Test Acc: 0.859\n"
     ]
    }
   ],
   "source": [
    "# Note that k: 3 provides the best accuracy where the classifier starts to stablize\n",
    "knn = KNeighborsClassifier(n_neighbors=3)\n",
    "knn.fit(X_train_scaled, y_train)\n",
    "print('k=3 Test Acc: %.3f' % knn.score(X_test_scaled, y_test))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "vscode": {
   "interpreter": {
    "hash": "9da66beca27f19bdf81de16e0121a30aa04d4edd5eb88e9a5610932102b72b6f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}