From 16e34b75fc37d53848e3f389db9a1daca345cd56 Mon Sep 17 00:00:00 2001
From: SFLazarus <35141055+SFLazarus@users.noreply.github.com>
Date: Tue, 29 Sep 2020 00:32:21 -0500
Subject: [PATCH] Delete project3.ipynb

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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Weekend movie trip\n",
-    "- EECS 731 Project 3\n",
-    "- Author: Lazarus\n",
-    "- ID : 3028051\n",
-    " \n",
-    "## Problem Statement\n",
-    "###  Blockbuster or art film?\n",
-    "1. Set up a data science project structure in a new git repository in your GitHub account\n",
-    "2. Download the one of the MovieLens datasets from https://grouplens.org/datasets/movielens/\n",
-    "3. Load the data set into panda data frames\n",
-    "4. Formulate one or two ideas on how the combination of ratings and tags by users helps the data set to establish additional value using exploratory data analysis\n",
-    "5. Build one or more clustering models to determine similar movies to recommend using the other ratings and tags of movies by other users as features\n",
-    "6. Document your process and results\n",
-    "7. Commit your notebook, source code, visualizations and other supporting files to the git repository in GitHub\n",
-    "\n",
-    "\n",
-    "## Data Description\n",
-    "This dataset (ml-latest-small) describes 5-star rating and free-text tagging activity from http://movielens.org, a movie recommendation service. It contains 100836 ratings and 3683 tag applications across 9742 movies. These data were created by 610 users between March 29, 1996 and September 24, 2018. Users were selected at random for inclusion. All selected users had rated at least 20 movies.Each user is represented by an id and had rated at least 20 movies.\n",
-    "The data are contained in the files `links.csv`, `movies.csv`, `ratings.csv` and `tags.csv`.\n",
-    "\n",
-    "\n",
-    "## What we want to do?\n",
-    "- Main goal: to use some Clustering models to recommend movies based on ratings and tags by other users.\n",
-    "- First we shall go through dataset and understand what features can we use to best form meaningful clusters.\n",
-    "\n",
-    "## Step 1: Lets prepare working environment\n",
-    "- Import all libraries required in the initial stage for data exploration and feature engineering\n",
-    "- Loading data from csv to a pandas dataframe\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 393,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "%matplotlib inline\n",
-    "\n",
-    "ratingsdata = pd.read_csv(\"../data/ratings.csv\")\n",
-    "moviesdata = pd.read_csv(\"../data/movies.csv\")\n",
-    "tagsdata = pd.read_csv(\"../data/tags.csv\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 2: Data Analysis and Feature Engineering\n",
-    "\n",
-    "### Let's go through what we have in our data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 394,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>title</th>\n",
-       "      <th>genres</th>\n",
-       "    </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  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(moviesdata.head(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### We have three features in 'movies.csv' - movieID which is consistent among all datasets, title and genres. I can see that all three features would help us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 395,
-   "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>userId</th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>tag</th>\n",
-       "      <th>timestamp</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2</td>\n",
-       "      <td>60756</td>\n",
-       "      <td>funny</td>\n",
-       "      <td>1445714994</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>60756</td>\n",
-       "      <td>Highly quotable</td>\n",
-       "      <td>1445714996</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>60756</td>\n",
-       "      <td>will ferrell</td>\n",
-       "      <td>1445714992</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>89774</td>\n",
-       "      <td>Boxing story</td>\n",
-       "      <td>1445715207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>89774</td>\n",
-       "      <td>MMA</td>\n",
-       "      <td>1445715200</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   userId  movieId              tag   timestamp\n",
-       "0       2    60756            funny  1445714994\n",
-       "1       2    60756  Highly quotable  1445714996\n",
-       "2       2    60756     will ferrell  1445714992\n",
-       "3       2    89774     Boxing story  1445715207\n",
-       "4       2    89774              MMA  1445715200"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(tagsdata.head(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### We have four features in 'tags.csv' - userID, movieID which are both consistent among all datasets, tag is comments made by users and timestamp represent seconds since midnight Coordinated Universal Time (UTC)\n",
-    "\n",
-    "#### We might not be using any of these for now, but tags can be something to work in future work, if we can use sentimental analysis or any other method to analyse these comments and generate a score for each tag by user, that would be very helpful for our recommendation model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 396,
-   "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>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"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(ratingsdata.head(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### We have four features in 'ratings.csv' - userID, movieID which are again consistent among all datasets, rating are made on a 5-star scale with half-star increments and timestamp represent seconds since midnight Coordinated Universal Time (UTC)\n",
-    "\n",
-    "#### Here we have rating which would play a major role in our recommendation model and also we have to figure out if we can use mean of rating for each movie or something else, let us first combine all three datasets, remove missing values and analyse it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 397,
-   "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>userId</th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>rating</th>\n",
-       "      <th>timestamp_x</th>\n",
-       "      <th>tag</th>\n",
-       "      <th>timestamp_y</th>\n",
-       "      <th>title</th>\n",
-       "      <th>genres</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>241</th>\n",
-       "      <td>2</td>\n",
-       "      <td>60756</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1445714980</td>\n",
-       "      <td>funny</td>\n",
-       "      <td>1.445715e+09</td>\n",
-       "      <td>Step Brothers (2008)</td>\n",
-       "      <td>Comedy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>2</td>\n",
-       "      <td>60756</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1445714980</td>\n",
-       "      <td>Highly quotable</td>\n",
-       "      <td>1.445715e+09</td>\n",
-       "      <td>Step Brothers (2008)</td>\n",
-       "      <td>Comedy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>243</th>\n",
-       "      <td>2</td>\n",
-       "      <td>60756</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1445714980</td>\n",
-       "      <td>will ferrell</td>\n",
-       "      <td>1.445715e+09</td>\n",
-       "      <td>Step Brothers (2008)</td>\n",
-       "      <td>Comedy</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>252</th>\n",
-       "      <td>2</td>\n",
-       "      <td>89774</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1445715189</td>\n",
-       "      <td>Boxing story</td>\n",
-       "      <td>1.445715e+09</td>\n",
-       "      <td>Warrior (2011)</td>\n",
-       "      <td>Drama</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>253</th>\n",
-       "      <td>2</td>\n",
-       "      <td>89774</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1445715189</td>\n",
-       "      <td>MMA</td>\n",
-       "      <td>1.445715e+09</td>\n",
-       "      <td>Warrior (2011)</td>\n",
-       "      <td>Drama</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     userId  movieId  rating  timestamp_x              tag   timestamp_y  \\\n",
-       "241       2    60756     5.0   1445714980            funny  1.445715e+09   \n",
-       "242       2    60756     5.0   1445714980  Highly quotable  1.445715e+09   \n",
-       "243       2    60756     5.0   1445714980     will ferrell  1.445715e+09   \n",
-       "252       2    89774     5.0   1445715189     Boxing story  1.445715e+09   \n",
-       "253       2    89774     5.0   1445715189              MMA  1.445715e+09   \n",
-       "\n",
-       "                    title  genres  \n",
-       "241  Step Brothers (2008)  Comedy  \n",
-       "242  Step Brothers (2008)  Comedy  \n",
-       "243  Step Brothers (2008)  Comedy  \n",
-       "252        Warrior (2011)   Drama  \n",
-       "253        Warrior (2011)   Drama  "
-      ]
-     },
-     "execution_count": 397,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tempdata = pd.merge(ratingsdata,tagsdata,on=['userId','movieId'],how='left')\n",
-    "mergeddata = pd.merge(tempdata,moviesdata,on=['movieId'],how='left').dropna()\n",
-    "mergeddata.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Let's Count the number of movies in each genres"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 398,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_word(df, ref_col, liste):\n",
-    "    keyword_count = dict()\n",
-    "    for s in liste: keyword_count[s] = 0\n",
-    "    for liste_keywords in df[ref_col].str.split('|'):\n",
-    "        if type(liste_keywords) == float and pd.isnull(liste_keywords): continue\n",
-    "        for s in liste_keywords: \n",
-    "            if pd.notnull(s): keyword_count[s] += 1\n",
-    "    # convert the dictionary in a list to sort the keywords  by frequency\n",
-    "    keyword_occurences = []\n",
-    "    for k,v in keyword_count.items():\n",
-    "        keyword_occurences.append([k,v])\n",
-    "    keyword_occurences.sort(key = lambda x:x[1], reverse = True)\n",
-    "    return keyword_occurences, keyword_count"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Let's make a set of all genres and count number of occurences using the function 'count_word' we just wrote"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 399,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['Drama', 2000],\n",
-       " ['Comedy', 1105],\n",
-       " ['Thriller', 1065],\n",
-       " ['Action', 788],\n",
-       " ['Crime', 779],\n",
-       " ['Sci-Fi', 641],\n",
-       " ['Romance', 585],\n",
-       " ['Adventure', 582],\n",
-       " ['Mystery', 388],\n",
-       " ['Fantasy', 283],\n",
-       " ['Animation', 231],\n",
-       " ['Horror', 209],\n",
-       " ['Children', 183],\n",
-       " ['War', 153],\n",
-       " ['IMAX', 152],\n",
-       " ['Musical', 117],\n",
-       " ['Documentary', 97],\n",
-       " ['Western', 59],\n",
-       " ['Film-Noir', 43],\n",
-       " ['(no genres listed)', 3]]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "genre_labels = set()\n",
-    "for s in mergeddata['genres'].str.split('|').values:\n",
-    "    genre_labels = genre_labels.union(set(s))\n",
-    "\n",
-    "keyword_occurences, dum = count_word(mergeddata, 'genres', genre_labels)\n",
-    "display(keyword_occurences)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Let's plot this observation to visualize better"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 400,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1296x936 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Graph the Genres vs Occurrences\n",
-    "fig = plt.figure(1, figsize=(18,13))\n",
-    "ax2 = fig.add_subplot(2,1,2)\n",
-    "y_axis = [i[1] for i in keyword_occurences]\n",
-    "x_axis = [k for k,i in enumerate(keyword_occurences)]\n",
-    "x_label = [i[0] for i in keyword_occurences]\n",
-    "plt.xticks(x_axis, x_label)\n",
-    "\n",
-    "plt.xticks(rotation=85, fontsize = 12)\n",
-    "plt.ylabel(\"No. of occurences\")\n",
-    "ax2.bar(x_axis, y_axis, align = 'center')\n",
-    "plt.title(\"Popularity of Genres\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Let's extract genres from 'genres' feature and make them as features to 'movisedata'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 401,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn import preprocessing\n",
-    "le = preprocessing.LabelEncoder()\n",
-    "genres_split = moviesdata.genres.str.split('|', expand=True)\n",
-    "genre_columns = np.unique(pd.DataFrame(pd.DataFrame(genres_split.values).values.flatten()).dropna().values.ravel())\n",
-    "genre_columns = np.sort(genre_columns)[::-1]\n",
-    "for g in genre_columns:\n",
-    "    moviesdata.insert(2, g,  [int(g in i) for i in moviesdata.genres.str.split('|', expand=False)], True)\n",
-    "del moviesdata['genres']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 402,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>title</th>\n",
-       "      <th>(no genres listed)</th>\n",
-       "      <th>Action</th>\n",
-       "      <th>Adventure</th>\n",
-       "      <th>Animation</th>\n",
-       "      <th>Children</th>\n",
-       "      <th>Comedy</th>\n",
-       "      <th>Crime</th>\n",
-       "      <th>Documentary</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Film-Noir</th>\n",
-       "      <th>Horror</th>\n",
-       "      <th>IMAX</th>\n",
-       "      <th>Musical</th>\n",
-       "      <th>Mystery</th>\n",
-       "      <th>Romance</th>\n",
-       "      <th>Sci-Fi</th>\n",
-       "      <th>Thriller</th>\n",
-       "      <th>War</th>\n",
-       "      <th>Western</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>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Jumanji (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Grumpier Old Men (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Waiting to Exhale (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</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>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 22 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   movieId                               title  (no genres listed)  Action  \\\n",
-       "0        1                    Toy Story (1995)                   0       0   \n",
-       "1        2                      Jumanji (1995)                   0       0   \n",
-       "2        3             Grumpier Old Men (1995)                   0       0   \n",
-       "3        4            Waiting to Exhale (1995)                   0       0   \n",
-       "4        5  Father of the Bride Part II (1995)                   0       0   \n",
-       "\n",
-       "   Adventure  Animation  Children  Comedy  Crime  Documentary  ...  Film-Noir  \\\n",
-       "0          1          1         1       1      0            0  ...          0   \n",
-       "1          1          0         1       0      0            0  ...          0   \n",
-       "2          0          0         0       1      0            0  ...          0   \n",
-       "3          0          0         0       1      0            0  ...          0   \n",
-       "4          0          0         0       1      0            0  ...          0   \n",
-       "\n",
-       "   Horror  IMAX  Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
-       "0       0     0        0        0        0       0         0    0        0  \n",
-       "1       0     0        0        0        0       0         0    0        0  \n",
-       "2       0     0        0        0        1       0         0    0        0  \n",
-       "3       0     0        0        0        1       0         0    0        0  \n",
-       "4       0     0        0        0        0       0         0    0        0  \n",
-       "\n",
-       "[5 rows x 22 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(moviesdata.head(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We have all genres as individual features and if a movie is part of it then the value is 1 and 0 if not. Now, let's add mean and number of ratings for each movie as a new feature"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 403,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def getAverageRating(movie_id):\n",
-    "    return ratingsdata.loc[ratingsdata['movieId'] == movie_id]['rating'].mean()\n",
-    "def getRatingCount(movie_id):\n",
-    "    return ratingsdata.loc[ratingsdata['movieId'] == movie_id]['rating'].count()\n",
-    "\n",
-    "average_ratings = moviesdata['movieId'].map(lambda x: getAverageRating(x))\n",
-    "rating_count = moviesdata['movieId'].map(lambda x: getRatingCount(x))\n",
-    "\n",
-    "moviesdata['Rating'] = average_ratings\n",
-    "moviesdata['Num_of_Ratings'] = rating_count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 404,
-   "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>movieId</th>\n",
-       "      <th>title</th>\n",
-       "      <th>(no genres listed)</th>\n",
-       "      <th>Action</th>\n",
-       "      <th>Adventure</th>\n",
-       "      <th>Animation</th>\n",
-       "      <th>Children</th>\n",
-       "      <th>Comedy</th>\n",
-       "      <th>Crime</th>\n",
-       "      <th>Documentary</th>\n",
-       "      <th>...</th>\n",
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-       "      <th>Musical</th>\n",
-       "      <th>Mystery</th>\n",
-       "      <th>Romance</th>\n",
-       "      <th>Sci-Fi</th>\n",
-       "      <th>Thriller</th>\n",
-       "      <th>War</th>\n",
-       "      <th>Western</th>\n",
-       "      <th>Rating</th>\n",
-       "      <th>Num_of_Ratings</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>Toy Story (1995)</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.920930</td>\n",
-       "      <td>215</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Jumanji (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.431818</td>\n",
-       "      <td>110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Grumpier Old Men (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.259615</td>\n",
-       "      <td>52</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Waiting to Exhale (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2.357143</td>\n",
-       "      <td>7</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>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.071429</td>\n",
-       "      <td>49</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   movieId                               title  (no genres listed)  Action  \\\n",
-       "0        1                    Toy Story (1995)                   0       0   \n",
-       "1        2                      Jumanji (1995)                   0       0   \n",
-       "2        3             Grumpier Old Men (1995)                   0       0   \n",
-       "3        4            Waiting to Exhale (1995)                   0       0   \n",
-       "4        5  Father of the Bride Part II (1995)                   0       0   \n",
-       "\n",
-       "   Adventure  Animation  Children  Comedy  Crime  Documentary  ...  IMAX  \\\n",
-       "0          1          1         1       1      0            0  ...     0   \n",
-       "1          1          0         1       0      0            0  ...     0   \n",
-       "2          0          0         0       1      0            0  ...     0   \n",
-       "3          0          0         0       1      0            0  ...     0   \n",
-       "4          0          0         0       1      0            0  ...     0   \n",
-       "\n",
-       "   Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western    Rating  \\\n",
-       "0        0        0        0       0         0    0        0  3.920930   \n",
-       "1        0        0        0       0         0    0        0  3.431818   \n",
-       "2        0        0        1       0         0    0        0  3.259615   \n",
-       "3        0        0        1       0         0    0        0  2.357143   \n",
-       "4        0        0        0       0         0    0        0  3.071429   \n",
-       "\n",
-       "   Num_of_Ratings  \n",
-       "0             215  \n",
-       "1             110  \n",
-       "2              52  \n",
-       "3               7  \n",
-       "4              49  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "display(moviesdata.head(5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 405,
-   "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",
-       "        vertical-align: top;\n",
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-       "\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>movieId</th>\n",
-       "      <th>title</th>\n",
-       "      <th>(no genres listed)</th>\n",
-       "      <th>Action</th>\n",
-       "      <th>Adventure</th>\n",
-       "      <th>Animation</th>\n",
-       "      <th>Children</th>\n",
-       "      <th>Comedy</th>\n",
-       "      <th>Crime</th>\n",
-       "      <th>Documentary</th>\n",
-       "      <th>...</th>\n",
-       "      <th>IMAX</th>\n",
-       "      <th>Musical</th>\n",
-       "      <th>Mystery</th>\n",
-       "      <th>Romance</th>\n",
-       "      <th>Sci-Fi</th>\n",
-       "      <th>Thriller</th>\n",
-       "      <th>War</th>\n",
-       "      <th>Western</th>\n",
-       "      <th>Rating</th>\n",
-       "      <th>Num_of_Ratings</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>314</th>\n",
-       "      <td>356</td>\n",
-       "      <td>Forrest Gump (1994)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.164134</td>\n",
-       "      <td>329</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>277</th>\n",
-       "      <td>318</td>\n",
-       "      <td>Shawshank Redemption, The (1994)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.429022</td>\n",
-       "      <td>317</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>257</th>\n",
-       "      <td>296</td>\n",
-       "      <td>Pulp Fiction (1994)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.197068</td>\n",
-       "      <td>307</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>510</th>\n",
-       "      <td>593</td>\n",
-       "      <td>Silence of the Lambs, The (1991)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.161290</td>\n",
-       "      <td>279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1939</th>\n",
-       "      <td>2571</td>\n",
-       "      <td>Matrix, The (1999)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.192446</td>\n",
-       "      <td>278</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 24 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      movieId                             title  (no genres listed)  Action  \\\n",
-       "314       356               Forrest Gump (1994)                   0       0   \n",
-       "277       318  Shawshank Redemption, The (1994)                   0       0   \n",
-       "257       296               Pulp Fiction (1994)                   0       0   \n",
-       "510       593  Silence of the Lambs, The (1991)                   0       0   \n",
-       "1939     2571                Matrix, The (1999)                   0       1   \n",
-       "\n",
-       "      Adventure  Animation  Children  Comedy  Crime  Documentary  ...  IMAX  \\\n",
-       "314           0          0         0       1      0            0  ...     0   \n",
-       "277           0          0         0       0      1            0  ...     0   \n",
-       "257           0          0         0       1      1            0  ...     0   \n",
-       "510           0          0         0       0      1            0  ...     0   \n",
-       "1939          0          0         0       0      0            0  ...     0   \n",
-       "\n",
-       "      Musical  Mystery  Romance  Sci-Fi  Thriller  War  Western    Rating  \\\n",
-       "314         0        0        1       0         0    1        0  4.164134   \n",
-       "277         0        0        0       0         0    0        0  4.429022   \n",
-       "257         0        0        0       0         1    0        0  4.197068   \n",
-       "510         0        0        0       0         1    0        0  4.161290   \n",
-       "1939        0        0        0       1         1    0        0  4.192446   \n",
-       "\n",
-       "      Num_of_Ratings  \n",
-       "314              329  \n",
-       "277              317  \n",
-       "257              307  \n",
-       "510              279  \n",
-       "1939             278  \n",
-       "\n",
-       "[5 rows x 24 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "moviesdata.sort_values(by=['Num_of_Ratings','Rating'], ascending=False, inplace=True)\n",
-    "display(moviesdata.head(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### It was really important to use 'number of ratings' because for instance some movie might have only 3 ratings of 3, 4 and 3, and other one with 40 ratings with a mean of 3.2, later one should be given precedence because there are more users involved which makes mean more meaningful.\n",
-    "\n",
-    "### Let's check and remove all missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 406,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "::::::::before:::::::::::\n",
-      "movieId                0\n",
-      "title                  0\n",
-      "(no genres listed)     0\n",
-      "Action                 0\n",
-      "Adventure              0\n",
-      "Animation              0\n",
-      "Children               0\n",
-      "Comedy                 0\n",
-      "Crime                  0\n",
-      "Documentary            0\n",
-      "Drama                  0\n",
-      "Fantasy                0\n",
-      "Film-Noir              0\n",
-      "Horror                 0\n",
-      "IMAX                   0\n",
-      "Musical                0\n",
-      "Mystery                0\n",
-      "Romance                0\n",
-      "Sci-Fi                 0\n",
-      "Thriller               0\n",
-      "War                    0\n",
-      "Western                0\n",
-      "Rating                18\n",
-      "Num_of_Ratings         0\n",
-      "dtype: int64\n",
-      "::::::::after::::::::::::\n",
-      "movieId               0\n",
-      "title                 0\n",
-      "(no genres listed)    0\n",
-      "Action                0\n",
-      "Adventure             0\n",
-      "Animation             0\n",
-      "Children              0\n",
-      "Comedy                0\n",
-      "Crime                 0\n",
-      "Documentary           0\n",
-      "Drama                 0\n",
-      "Fantasy               0\n",
-      "Film-Noir             0\n",
-      "Horror                0\n",
-      "IMAX                  0\n",
-      "Musical               0\n",
-      "Mystery               0\n",
-      "Romance               0\n",
-      "Sci-Fi                0\n",
-      "Thriller              0\n",
-      "War                   0\n",
-      "Western               0\n",
-      "Rating                0\n",
-      "Num_of_Ratings        0\n",
-      "dtype: int64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"::::::::before:::::::::::\")\n",
-    "print(moviesdata.isnull().sum())\n",
-    "moviesdata=moviesdata.dropna()\n",
-    "print(\"::::::::after::::::::::::\")\n",
-    "print(moviesdata.isnull().sum())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let us select all genres,  number of rating and mean rating as 'clusterFactors', which can be input for clustering model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 407,
-   "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>(no genres listed)</th>\n",
-       "      <th>Action</th>\n",
-       "      <th>Adventure</th>\n",
-       "      <th>Animation</th>\n",
-       "      <th>Children</th>\n",
-       "      <th>Comedy</th>\n",
-       "      <th>Crime</th>\n",
-       "      <th>Documentary</th>\n",
-       "      <th>Drama</th>\n",
-       "      <th>Fantasy</th>\n",
-       "      <th>Film-Noir</th>\n",
-       "      <th>Horror</th>\n",
-       "      <th>IMAX</th>\n",
-       "      <th>Musical</th>\n",
-       "      <th>Mystery</th>\n",
-       "      <th>Romance</th>\n",
-       "      <th>Sci-Fi</th>\n",
-       "      <th>Thriller</th>\n",
-       "      <th>War</th>\n",
-       "      <th>Western</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>314</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>277</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
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-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>257</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>510</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1939</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      (no genres listed)  Action  Adventure  Animation  Children  Comedy  \\\n",
-       "314                    0       0          0          0         0       1   \n",
-       "277                    0       0          0          0         0       0   \n",
-       "257                    0       0          0          0         0       1   \n",
-       "510                    0       0          0          0         0       0   \n",
-       "1939                   0       1          0          0         0       0   \n",
-       "\n",
-       "      Crime  Documentary  Drama  Fantasy  Film-Noir  Horror  IMAX  Musical  \\\n",
-       "314       0            0      1        0          0       0     0        0   \n",
-       "277       1            0      1        0          0       0     0        0   \n",
-       "257       1            0      1        0          0       0     0        0   \n",
-       "510       1            0      0        0          0       1     0        0   \n",
-       "1939      0            0      0        0          0       0     0        0   \n",
-       "\n",
-       "      Mystery  Romance  Sci-Fi  Thriller  War  Western  \n",
-       "314         0        1       0         0    1        0  \n",
-       "277         0        0       0         0    0        0  \n",
-       "257         0        0       0         1    0        0  \n",
-       "510         0        0       0         1    0        0  \n",
-       "1939        0        0       1         1    0        0  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "clusterFactors = moviesdata.drop(columns=['Num_of_Ratings', 'Rating', 'title', 'movieId'])\n",
-    "display(clusterFactors.head(5))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Step 3: Clustering movies and develop recommendation model\n",
-    "\n",
-    "### First we will use KMeans Clustering model\n",
-    "- let us find the optimum K value first.\n",
-    "- There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 408,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from sklearn.cluster import KMeans\n",
-    "from scipy.spatial.distance import cdist\n",
-    "distances = []\n",
-    "K = range(1,100,3)\n",
-    "for k in K:\n",
-    "    kmeanModel = KMeans(n_clusters=k).fit(clusterFactors)\n",
-    "    kmeanModel.fit(clusterFactors)\n",
-    "    distances.append(sum(np.min(cdist(clusterFactors, kmeanModel.cluster_centers_, 'euclidean'), axis=1)) / clusterFactors.shape[0])\n",
-    "\n",
-    "# Plot the elbow graph\n",
-    "plt.plot(K, distances, 'kx-')\n",
-    "plt.xlabel('k')\n",
-    "plt.ylabel('Distance')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster. We can see from the plot that we have optimal k value as 20. Let's use 20 and train the model\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 409,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kmeans = KMeans(n_clusters=20).fit_predict(clusterFactors)\n",
-    "moviesdata['KMeanCluster'] = kmeans"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Now let us sort our dataframe over 'KMeanCluster' , 'Num of ratings' and 'Rating'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 410,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>movieId</th>\n",
-       "      <th>title</th>\n",
-       "      <th>(no genres listed)</th>\n",
-       "      <th>Action</th>\n",
-       "      <th>Adventure</th>\n",
-       "      <th>Animation</th>\n",
-       "      <th>Children</th>\n",
-       "      <th>Comedy</th>\n",
-       "      <th>Crime</th>\n",
-       "      <th>Documentary</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Musical</th>\n",
-       "      <th>Mystery</th>\n",
-       "      <th>Romance</th>\n",
-       "      <th>Sci-Fi</th>\n",
-       "      <th>Thriller</th>\n",
-       "      <th>War</th>\n",
-       "      <th>Western</th>\n",
-       "      <th>Rating</th>\n",
-       "      <th>Num_of_Ratings</th>\n",
-       "      <th>KMeanCluster</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Usual Suspects, The (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.237745</td>\n",
-       "      <td>204</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>509</th>\n",
-       "      <td>592</td>\n",
-       "      <td>Batman (1989)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.428571</td>\n",
-       "      <td>189</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>793</th>\n",
-       "      <td>1036</td>\n",
-       "      <td>Die Hard (1988)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.862069</td>\n",
-       "      <td>145</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>165</td>\n",
-       "      <td>Die Hard: With a Vengeance (1995)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3.555556</td>\n",
-       "      <td>144</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>828</th>\n",
-       "      <td>1089</td>\n",
-       "      <td>Reservoir Dogs (1992)</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>4.202290</td>\n",
-       "      <td>131</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     movieId                              title  (no genres listed)  Action  \\\n",
-       "46        50         Usual Suspects, The (1995)                   0       0   \n",
-       "509      592                      Batman (1989)                   0       1   \n",
-       "793     1036                    Die Hard (1988)                   0       1   \n",
-       "138      165  Die Hard: With a Vengeance (1995)                   0       1   \n",
-       "828     1089              Reservoir Dogs (1992)                   0       0   \n",
-       "\n",
-       "     Adventure  Animation  Children  Comedy  Crime  Documentary  ...  Musical  \\\n",
-       "46           0          0         0       0      1            0  ...        0   \n",
-       "509          0          0         0       0      1            0  ...        0   \n",
-       "793          0          0         0       0      1            0  ...        0   \n",
-       "138          0          0         0       0      1            0  ...        0   \n",
-       "828          0          0         0       0      1            0  ...        0   \n",
-       "\n",
-       "     Mystery  Romance  Sci-Fi  Thriller  War  Western    Rating  \\\n",
-       "46         1        0       0         1    0        0  4.237745   \n",
-       "509        0        0       0         1    0        0  3.428571   \n",
-       "793        0        0       0         1    0        0  3.862069   \n",
-       "138        0        0       0         1    0        0  3.555556   \n",
-       "828        1        0       0         1    0        0  4.202290   \n",
-       "\n",
-       "     Num_of_Ratings  KMeanCluster  \n",
-       "46              204            19  \n",
-       "509             189            19  \n",
-       "793             145            19  \n",
-       "138             144            19  \n",
-       "828             131            19  \n",
-       "\n",
-       "[5 rows x 25 columns]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "moviesdata.sort_values(by=['KMeanCluster','Num_of_Ratings', 'Rating'], ascending=False, inplace=True)\n",
-    "display(moviesdata.head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Lets write a function to find 5 similar movies with KMean Clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 411,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['Toy Story (1995)'],\n",
-       " ['Lord of the Rings: The Fellowship of the Ring, The (2001)'],\n",
-       " ['Lord of the Rings: The Two Towers, The (2002)'],\n",
-       " ['Lion King, The (1994)'],\n",
-       " ['Shrek (2001)']]"
-      ]
-     },
-     "execution_count": 411,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def findSimilarMovie(movieTitle):\n",
-    "    cluster = moviesdata.loc[moviesdata['title'] == movieTitle]['KMeanCluster'].values[0]\n",
-    "    cluster_movies = moviesdata.loc[moviesdata['KMeanCluster'] == cluster]\n",
-    "    return pd.DataFrame(cluster_movies['title'].values).head(5).values.tolist()\n",
-    "findSimilarMovie('Toy Story (1995)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 412,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['American Beauty (1999)'],\n",
-       " ['Good Will Hunting (1997)'],\n",
-       " ['Titanic (1997)'],\n",
-       " ['Eternal Sunshine of the Spotless Mind (2004)'],\n",
-       " ['Beautiful Mind, A (2001)']]"
-      ]
-     },
-     "execution_count": 412,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "findSimilarMovie('Titanic (1997)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We have a successful movie recommender based on Kmeans Clustering\n",
-    "\n",
-    "## Let's try Agglomerative Clustering "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 413,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cluster import AgglomerativeClustering\n",
-    "agglomerative_clustering = AgglomerativeClustering(n_clusters=20).fit_predict(clusterFactors)\n",
-    "moviesdata['AgglomerativeClustering'] = agglomerative_clustering\n",
-    "moviesdata.sort_values(by=['AgglomerativeClustering', 'Num_of_Ratings', 'Rating'], inplace=True, ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### We will write a similar movie recommender funciton based on Agglomerative Clustering"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 414,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def findSimilarMovieAC(movieTitle):\n",
-    "    cluster = moviesdata.loc[moviesdata['title'] == movieTitle]['AgglomerativeClustering'].values[0]\n",
-    "    cluster_movies = moviesdata.loc[moviesdata['AgglomerativeClustering'] == cluster]\n",
-    "    return pd.DataFrame(cluster_movies['title'].values).head(5).values.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 415,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['Terminator 2: Judgment Day (1991)'],\n",
-       " ['Toy Story (1995)'],\n",
-       " ['Mask, The (1994)'],\n",
-       " ['Beauty and the Beast (1991)'],\n",
-       " ['Inception (2010)']]"
-      ]
-     },
-     "execution_count": 415,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "findSimilarMovieAC('Toy Story (1995)')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 416,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['Beautiful Mind, A (2001)'],\n",
-       " ['Rob Roy (1995)'],\n",
-       " ['Pearl Harbor (2001)'],\n",
-       " ['Bodyguard, The (1992)'],\n",
-       " ['About Time (2013)']]"
-      ]
-     },
-     "execution_count": 416,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "findSimilarMovie('Titanic (1997)')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## We have another successful recommender based on Agglomerative Clustering\n",
-    "\n",
-    "## Now let us plot these clusters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 417,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Movie Id')"
-      ]
-     },
-     "execution_count": 417,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20,5))\n",
-    "plt.scatter(moviesdata['movieId'], moviesdata['KMeanCluster'])\n",
-    "\n",
-    "plt.ylabel(\"K Mean Clusters\", fontsize= 15)\n",
-    "plt.xlabel(\"Movie Id\", fontsize= 15)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 418,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0, 'Movie Id')"
-      ]
-     },
-     "execution_count": 418,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(20,5))\n",
-    "plt.scatter(moviesdata['movieId'], moviesdata['AgglomerativeClustering'])\n",
-    "\n",
-    "plt.ylabel(\"Agglomerative Clusters\", fontsize= 15)\n",
-    "plt.xlabel(\"Movie Id\", fontsize= 15)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusions and further improvements:\n",
-    "\n",
-    "- We can get more meaningful recommendations if we could use tags and score them for clustering\n",
-    "- Also I'm not sure if that would improve but we can try combinations of multiple clustering models to recommend movies."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}