Movie_Embeddings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e41b24c",
   "metadata": {},
   "source": [
    "### **Movie Embeddings**\n",
    "\n",
    "Instead of embedding words, we will embed movies.  In particular, if we can embed movies, then similar movies will be close to each other and can be recommended.  This line of reasoning  is analogous to the [distributional hypothesis of word meanings]. For words, this roughly translates to words that appear in similar sentences should have similar vector representations. For movies, vectors for two movies should be similar if they are watched by similar people. \n",
    "\n",
    "Let the total number of movies be $M$. Let $X_{i,j}$ be the number of users that liked both movies $i$ and $j$. We want to obtain vectors $v_1,...,v_i,...,v_j,...,v_M$ for all movies such that we minimize the cost $c(v_1,...,v_M) = \\sum_{i=1}^{M}\\sum_{j=1}^{M}\\mathbf{1}_{[i\\neq j]}(v_i^Tv_j - X_{i,j})^2$. Here $\\mathbf{1}_{[i\\neq j]}$ is a function that is $0$ when $i=j$ and $1$ otherwise.\n",
    "\n",
    "\n",
    " -  **Compute data $X_{i,j}$ from the movielens (small) [dataset] and [description]. Briefly describe your data prep workflow (you can use `pandas` if needed).**\n",
    "\n",
    " -  **Optimize function $c(v_1,...,v_M)$ over $v_1,...,v_M$ using gradient descent (using `pytorch` or `tensorflow`). Plot the loss as a function of iteration for various choices (learning rates, choice of optimizers etc).**\n",
    " -  **Recommend top 10 movies (not vectors or indices but movie names) given movies (a) _Apollo 13_, (b) _Toy Story_, and (c) _Home Alone_ . Describe your recommendation strategy. Do the recommendations change when you change learning rates or optimizers? Why or why not?**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2f9e716",
   "metadata": {},
   "source": [
    "**PART A:** Compute data $X_{i,j}$ from the movielens (small) dataset and description. Briefly describe your data prep workflow (you can use pandas if needed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7d5d2e4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Importing the libraries\n",
    "import pandas as pd\n",
    "from math import sqrt\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import seaborn as sns\n",
    "from scipy.sparse import csr_matrix\n",
    "from sklearn.neighbors import NearestNeighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ab80d401",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TV\\AppData\\Local\\Temp/ipykernel_10000/1767327448.py:7: FutureWarning: The default value of regex will change from True to False in a future version.\n",
      "  movies_dataframe['Movie_Title'] = movies_dataframe.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movieId</th>\n",
       "      <th>title</th>\n",
       "      <th>genres</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>Movie_Title</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>[Adventure, Animation, Children, Comedy, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>Toy Story (1995)</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",
       "      <td>[Adventure, Children, Fantasy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>Jumanji (1995)</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",
       "      <td>[Comedy, Romance]</td>\n",
       "      <td>1995</td>\n",
       "      <td>Grumpier Old Men (1995)</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",
       "      <td>[Comedy, Drama, Romance]</td>\n",
       "      <td>1995</td>\n",
       "      <td>Waiting to Exhale (1995)</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",
       "      <td>[Comedy]</td>\n",
       "      <td>1995</td>\n",
       "      <td>Father of the Bride Part II (1995)</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   \n",
       "\n",
       "                                               Genre  Year  \\\n",
       "0  [Adventure, Animation, Children, Comedy, Fantasy]  1995   \n",
       "1                     [Adventure, Children, Fantasy]  1995   \n",
       "2                                  [Comedy, Romance]  1995   \n",
       "3                           [Comedy, Drama, Romance]  1995   \n",
       "4                                           [Comedy]  1995   \n",
       "\n",
       "                          Movie_Title  \n",
       "0                    Toy Story (1995)  \n",
       "1                      Jumanji (1995)  \n",
       "2             Grumpier Old Men (1995)  \n",
       "3            Waiting to Exhale (1995)  \n",
       "4  Father of the Bride Part II (1995)  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Reading movies and ratings data into dataframes from csv \n",
    "movies_dataframe = pd.read_csv(\"movies.csv\")\n",
    "ratings_dataframe = pd.read_csv(\"ratings.csv\")\n",
    "#putting the year in a new column after being removed from the title column\n",
    "movies_dataframe['Genre'] = movies_dataframe.genres.str.split('|')\n",
    "movies_dataframe['Year'] = movies_dataframe.title.str.extract('(\\(\\d\\d\\d\\d\\))',expand=False)\n",
    "movies_dataframe['Movie_Title'] = movies_dataframe.title.str.replace('(\\(\\d\\d\\d\\d\\))', '')\n",
    "movies_dataframe['Year'] = movies_dataframe.Year.str.extract('(\\d\\d\\d\\d)',expand=False)\n",
    "movies_dataframe['Movie_Title'] = movies_dataframe['title'].apply(lambda x: x.strip())\n",
    "length_of_df= len(movies_dataframe)\n",
    "#taking care of the values in the Genre column by splitting\n",
    "Genre_movie = movies_dataframe\n",
    "#printing the dataframe\n",
    "movies_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3bcb365f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        userId  movieId  rating\n",
      "0            1        1     4.0\n",
      "1            1        3     4.0\n",
      "2            1        6     4.0\n",
      "3            1       47     5.0\n",
      "4            1       50     5.0\n",
      "...        ...      ...     ...\n",
      "100831     610   166534     4.0\n",
      "100832     610   168248     5.0\n",
      "100833     610   168250     5.0\n",
      "100834     610   168252     5.0\n",
      "100835     610   170875     3.0\n",
      "\n",
      "[100836 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\TV\\AppData\\Local\\Temp/ipykernel_10000/2458763020.py:9: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  Ratings = ratings_dataframe.drop('timestamp', 1)\n"
     ]
    }
   ],
   "source": [
    "#We individually consider the genres in order to use them as a foundation for matching the movies to particular audiences.\n",
    "for i, j in movies_dataframe.iterrows():\n",
    "    for g in j['Genre']:\n",
    "        Genre_movie.at[i, g] = 1\n",
    "\n",
    "#Replacing na with 0\n",
    "Genre_movie = Genre_movie.fillna(0)\n",
    "# Time stamps are removed since we don't require them\n",
    "Ratings = ratings_dataframe.drop('timestamp', 1)\n",
    "print(Ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "0fe8cfb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Users: 610\n"
     ]
    }
   ],
   "source": [
    "#Finding and printing the number of Users\n",
    "users = Ratings[\"userId\"]\n",
    "user_uniq = users.unique()\n",
    "userid = user_uniq.tolist()\n",
    "user_user_en = {i: x for i, x in enumerate(userid)}\n",
    "user_user = {x: i for i, x in enumerate(userid)}\n",
    "Ratings[\"user\"] = Ratings[\"userId\"].map(user_user)\n",
    "no_of_users = len(user_user)\n",
    "print(\"Number of Users: {}\".format(no_of_users))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "0ed4ac99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Movies: 9724\n"
     ]
    }
   ],
   "source": [
    "#Finding and printing the number of Movies\n",
    "movies = Ratings[\"movieId\"]\n",
    "movies_uniq= movies.unique()\n",
    "moviesid= movies_uniq.tolist()  \n",
    "movie_movie_en = {i: x for i, x in enumerate(moviesid)}\n",
    "movie_movie = {x: i for i, x in enumerate(moviesid)}\n",
    "Ratings[\"movie\"] = Ratings[\"movieId\"].map(movie_movie)\n",
    "no_of_movies = len(movie_movie_en)\n",
    "print(\"Number of Movies: {}\".format(no_of_movies))\n",
    "Ratings[\"rating\"] = Ratings[\"rating\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e65db3c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum Rating: 0.5\n",
      "Maximum Rating: 5.0\n"
     ]
    }
   ],
   "source": [
    "#Normalizing the ratings\n",
    "minimum_rate = min(Ratings[\"rating\"])\n",
    "maximum_rate = max(Ratings[\"rating\"])\n",
    "#Random Sampling the data\n",
    "Ratings = Ratings.sample(frac=1, random_state=10)\n",
    "X_ind = Ratings[[\"user\", \"movie\"]].values\n",
    "#Printing the minimum and maximum ratings among all\n",
    "print(\"Minimum Rating: {}\".format(minimum_rate))\n",
    "print(\"Maximum Rating: {}\".format(maximum_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "de466aa8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Normalizing the target variable\n",
    "Y_lab = Ratings[\"rating\"].apply(lambda x: (x - minimum_rate) / (maximum_rate - minimum_rate)).values\n",
    "#Splitting the data into Training Data: 80% and Validation Data: 20%\n",
    "train = int(0.8 * Ratings.shape[0])\n",
    "#Spitting the training and validation data\n",
    "x_train, x_valid, y_train, y_valid = (X_ind[:train], X_ind[train:], Y_lab[:train], Y_lab[train:],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f660dd09",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a recommend class for our recommender system\n",
    "Emb = 50 # Taking the embedding size as 50\n",
    "class recommend(keras.Model):\n",
    "  def __init__(self, no_of_users, no_of_movies, emb, **kwargs):\n",
    "    super(recommend, self).__init__(**kwargs)\n",
    "    self.user_emb = layers.Embedding(\n",
    "            no_of_users,\n",
    "            emb,\n",
    "            embeddings_initializer=\"he_normal\",\n",
    "            embeddings_regularizer=keras.regularizers.l2(1e-6),\n",
    "        )\n",
    "    self.movie_emb = layers.Embedding(\n",
    "            no_of_movies,\n",
    "            emb,\n",
    "            embeddings_initializer=\"he_normal\",\n",
    "            embeddings_regularizer=keras.regularizers.l2(1e-6),\n",
    "        )\n",
    "    self.user_bias = layers.Embedding(no_of_users, 1)\n",
    "    self.movie_bias = layers.Embedding(no_of_movies, 1)\n",
    "    self.no_of_users = no_of_users\n",
    "    self.no_of_movies = no_of_movies\n",
    "    self.emb = emb\n",
    "  # Creating a function to combine all the components\n",
    "  def call(self, inputs):\n",
    "    u_vector = self.user_emb(inputs[:, 0])\n",
    "    m_vector = self.movie_emb(inputs[:, 1])\n",
    "    u_bias = self.user_bias(inputs[:, 0])\n",
    "    m_bias = self.movie_bias(inputs[:, 1])\n",
    "    user_mov_td= tf.tensordot(u_vector, m_vector, 2)\n",
    "    res = u_bias + m_bias + user_mov_td\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "a57fc32a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the Adam model with learning rate= 0.0001 and compiling it\n",
    "model_adam = recommend(no_of_users, no_of_movies, Emb) \n",
    "model_adam.compile(loss = tf.keras.losses.MeanAbsoluteError(), optimizer = keras.optimizers.Adam(learning_rate = 0.0001))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "bdd4cd36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "2521/2521 - 15s - loss: 0.6081 - val_loss: 0.5297 - 15s/epoch - 6ms/step\n",
      "Epoch 2/7\n",
      "2521/2521 - 13s - loss: 0.3009 - val_loss: 0.2190 - 13s/epoch - 5ms/step\n",
      "Epoch 3/7\n",
      "2521/2521 - 13s - loss: 0.1824 - val_loss: 0.2325 - 13s/epoch - 5ms/step\n",
      "Epoch 4/7\n",
      "2521/2521 - 13s - loss: 0.1690 - val_loss: 0.2253 - 13s/epoch - 5ms/step\n",
      "Epoch 5/7\n",
      "2521/2521 - 13s - loss: 0.1623 - val_loss: 0.2230 - 13s/epoch - 5ms/step\n",
      "Epoch 6/7\n",
      "2521/2521 - 13s - loss: 0.1580 - val_loss: 0.2139 - 13s/epoch - 5ms/step\n",
      "Epoch 7/7\n",
      "2521/2521 - 13s - loss: 0.1548 - val_loss: 0.2139 - 13s/epoch - 5ms/step\n"
     ]
    }
   ],
   "source": [
    "#Fitting the Adam model with batch size=32 and epoch=7\n",
    "hist_adam = model_adam.fit(x=x_train, y=y_train, batch_size=32, epochs=7, verbose=2,validation_data=(x_valid, y_valid),)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0e0084e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating the SGD model with learning rate= 0.001 and compiling it\n",
    "model_sgd= recommend(no_of_users, no_of_movies, Emb) \n",
    "model_sgd.compile( loss = tf.keras.losses.MeanAbsoluteError(), optimizer = keras.optimizers.SGD(learning_rate = 0.001)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2b1c6418",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7\n",
      "2521/2521 - 13s - loss: 0.6634 - val_loss: 0.6533 - 13s/epoch - 5ms/step\n",
      "Epoch 2/7\n",
      "2521/2521 - 12s - loss: 0.6420 - val_loss: 0.6376 - 12s/epoch - 5ms/step\n",
      "Epoch 3/7\n",
      "2521/2521 - 12s - loss: 0.6209 - val_loss: 0.6221 - 12s/epoch - 5ms/step\n",
      "Epoch 4/7\n",
      "2521/2521 - 12s - loss: 0.6003 - val_loss: 0.6070 - 12s/epoch - 5ms/step\n",
      "Epoch 5/7\n",
      "2521/2521 - 12s - loss: 0.5802 - val_loss: 0.5923 - 12s/epoch - 5ms/step\n",
      "Epoch 6/7\n",
      "2521/2521 - 12s - loss: 0.5608 - val_loss: 0.5782 - 12s/epoch - 5ms/step\n",
      "Epoch 7/7\n",
      "2521/2521 - 12s - loss: 0.5422 - val_loss: 0.5647 - 12s/epoch - 5ms/step\n"
     ]
    }
   ],
   "source": [
    "#Fitting the SGD model with batch size=32 and epoch=7\n",
    "hist_sgd = model_sgd.fit( x=x_train, y=y_train, batch_size=32, epochs=7, verbose=2, validation_data=(x_valid, y_valid),)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "23395df3",
   "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": [
    "#Plotting the loss v/s epoch for Adam model\n",
    "plt.title(\"Plotting Adam model's loss v/s epoch\", fontweight='bold')\n",
    "plt.plot(hist_adam.history[\"loss\"])\n",
    "plt.plot(hist_adam.history[\"val_loss\"])\n",
    "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n",
    "plt.ylabel(\"LOSS\", fontweight='bold')\n",
    "plt.xlabel(\"EPOCH\", fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5757e4f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qdylRogRt2rShXr16jB079m/LunXrRmxsLHXq1GHcuHG0bNkyS2NzZn3JKawG5gS+9jxH4cAWY0wMcFREDmIljHBgl0P11AKgJfCtE+NNUYuqJVg6oh3//m0fX645zOr955n0SCPqlCvsqpCUUm5i5syZSc7PmzcvS5cuTXJZQjtDyZIlCQkJ+XP+mDFjMi0uZ5YgtgE1RKSKiHgDjwILE62zAKv0gIiUxKpaOmJvW1REEooFnfh724VLFPLxYmKfBkwbEMCF67fp+dkGvlp7mLh4vblOKeV+nJYgjDGxwDBgObAPmG2MCRWRd0Skh73aciBSRPYCq4GxxphIY0wcVvXSHyKyBxBgqrNivVP3+Jdh+cj2dKpdmveW7ueRrzdxPFK76lBKuRdxl64lAgICTFaPSW2MYcGuU7z1ayhx8YY37vOnX/OKuaLjL6Xcxb59+6hTp46rw8gSSZ2riGw3xgQktb6rG6lzNBGhV2Nflo9sT+NKRXlt/h6emrGN81e1qw6lVM6nCSITlC+ajx+fasHbD/iz8XAkXSYHsjj4jKvDUkqpDNEEkUny5BEGtqnC4uHtqFw8Py/M3MGIWTu5cjPG1aEppVS6aIIAuJp5v/arly7I3OdbM+qemiwOPkPXyYEEHsz8m/iUUu4hvd19A0yePJmbN29mckR/0QRx7SxMaQwzH4UzuzNll54eeRhxTw3mDW1NQR9PBkzfypsLQrgZnXL3vUqp3Cc7JwjtWMi7ALR7CTZ9Cl+3h9r3Q8dXoWy9DO+6gW9RfnuxLR8sP8C364+yPuwCH/VtSJNKxTIhcKWUO3Ds7rtz586ULl2a2bNnc/v2bXr16sX48eO5ceMGffv2JTw8nLi4ON58803OnTvH6dOnueuuuyhZsiSrV6/O9Ng0QeQtBB3GQoshsPlL2PQF7P8N/HtCh3FQxj/1faTAx8uDN+/35+46pRn7SzB9vtzI0I7VGX53Dbw9tQCnVLaydByc3ZO5+yxbH+79x0gHf3Ls7nvFihXMmTOHrVu3YoyhR48eBAYGEhERQfny5Vm8eDFg9dFUpEgRPv74Y1avXk3JkiUzN2abfkMl8CkCHcfByN3Q/mUIWwVftoZfBkHEgQzvvnW1kiwb2Y7eTXz5bHUYD36+gQNnk+6gSymVO61YsYIVK1bQuHFjmjRpwv79+zl06BD169dn5cqVvPLKK6xbt44iRYpkSTxagkgsXzHo9Dq0fB42fQabv4LQ+VC/D3R4BUrWSPeuC/l48cHDDensX4ZX5+3hgU/XM6ZrTZ5uWxWPPHpznVIul8Iv/axgjOHVV1/l2Wef/ceyHTt2sGTJEt544w3uvvtu3nrrLafHoyWI5OQvDne/BSP3QJsRsH8xfN4c5j0LkYcztOsudcuyfFR7OtYqxYQl++k3dTMnLzqvoUkplX05dvfdtWtXpk+fzvXr1hA4p06d4vz585w+fZr8+fPTv39/xo4dy44dO/6xrTNogkhNgRLQeTyMCIZWL8DeX+GzZrBgKFw8mu7dliyYl6+faMqHDzdk3+mrdJscyP+2ncBduj5RSqWNY3ffK1eu5LHHHqNVq1bUr1+fPn36cO3aNfbs2UPz5s1p1KgR48eP54033gBgyJAhdOvWjbvuusspsWlfTHfq2jnY8AkEfQvxsdCwH7QfC8Uqp3uX4ZduMvaXYDYdieTu2qV5t3d9ShfyycSglVLJ0b6YtC+mzFOoDHSbACN2Q7NnIHg2fNoEFo2AyydT3z4JvsXy89MzLXjrfn/Wh12g66RAlu7RrjqUUq6lCSK9CpWFeyfCiF3QdBDsmmndcLf4JbiSeFyk1OXJIzzVtgqLh7fFt1h+nv9pB6P/t4srt7SrDqWUa2iCyKjC5eG+D+HFHdC4P2z/HqY0giUvp6sLj+qlCzFvaGtG3F2DX3efptvkQNYfupD5cSul/uQuVe0pSc85aoLILEUrwgOT4cXt0PBR2DbNShTLXrXaLe6Al0ceRnWuybznW5PP24P+327h7YWh3IrWcbCVymw+Pj5ERka6dZIwxhAZGYmPz521bWojtbNcPAqBH8Lun8HDG5o9DW1GQsFSqW7qKComjonL9vPdhmNULVWAj/s2olHFok4JWancKCYmhvDwcKKi3HscFx8fH3x9ffHy8vrb/JQaqTVBOFvkYQj8AIL/B54+0HwwtB5hXT57BzaGXWDML7s5d+02L9xVnRc7VcfLQwuASqmM0QSRHVw4BGvfhz2/WB0ENh8CrV+0bshLo6tRMby9MJR5O05Rr0JhJvVtRI0yhZwYtFLK3WmCyE4iDsDaiRAyD7wLQsvnrBvw8qW9h9dlIWd4bX4I12/H8nLXWjzVpgp5tKsOpVQ6aILIjs7vgzXvwd4FkLcwtBxq9f+Ur2iaNo+4dptX5wXz+77ztKxanA8fbohvsfxODVkp5X40QWRnZ0NgzbtWF+M+RaDVi9DiWfApnOqmxhh+2R7OO4v2AvCvB/zp09QXES1NKKXSRhNETnBmt1WiOLDEqm5q/aLVTpE39TaGkxdvMuaX3Ww5epHO/mWY0Ks+pQrlzYKglVI5ncu62hCRbiJyQETCRGRcMuv0FZG9IhIqIjMTLSssIuEi8pkz48wWyjWEfj/D4NXg2xz+eAcmN4D1kyH6RoqbViyen58Ht+SN++qw9mAEXSatZXGwdtWhlMoYp5UgRMQDOAh0BsKBbUA/Y8xeh3VqALOBTsaYSyJS2hhz3mH5J0Ap4KIxZlhKx8vxJYjEwrfDmgkQ9jvkLwltR0LA0+CdcjtD2PlrvDR7N7vDr/BAw/K806MuxQp4Z03MSqkcx1UliOZAmDHmiDEmGpgF9Ey0zmDgc2PMJYBEyaEpUAZY4cQYsy/fptB/Ljy90hqycMUb8ElDa0jUmFvJbla9dCHmPt+aMV1qsizkDF0mB/L73ju7k1sppcC5CaIC4Ni9abg9z1FNoKaIbBCRzSLSDUBE8gAfAWNSOoCIDBGRIBEJioiIyMTQs5GKzWHAAhi0DErXhuWvwieNYMvXEJP0nZ+eHnkY1qkGv77QlhIFvHnmhyBemr1bO/5TSt0RV9+K6wnUADoC/YCpIlIUGAosMcaEp7SxMeYbY0yAMSagVKk768Iix6ncCp5cBAMXQ4lqsPRlq/fYbdMg9naSm/iXL8zCYW15sVN1Fuw6RbfJgQQedNNEqpTKdM5MEKeAig7TvvY8R+HAQmNMjDHmKFabRQ2gFTBMRI4BHwIDRMS1g8VmF35trSQxYCEUrWR1Lz6lCQR9B7HR/1jd2zMPL3WpxbznW1MgrycDpm/ltfl7uH471gXBK6VyEmc2UntifeHfjZUYtgGPGWNCHdbphtVw/aSIlAR2Ao2MMZEO6wwEAnJdI3VaGANHVsPqCRC+DYpUgvZjoNFj4OH1j9WjYuL4eOVBpq47gm+xfHzQpyEtq95Zn1BKKffikkZqY0wsMAxYDuwDZhtjQkXkHRHpYa+2HIgUkb3AamCsY3JQqRCBap2shuzH50KBkrBouDXC3fYZ/yhR+Hh58Fr3OvzybCs8RHj0m82MX6TdiCulkqY3yrkTY+DQSlj7HpzabpUo2o2CRv3B8++Xut6MjmXi0v18v+k4VUoW4MOHG9K0ctr7g1JKuQe9kzq3MQbC/rASRfg2KOxrJYrGT4Dn3++w3hh2gbFzgjlz5RZD2ldjVOca5PX0cFHgSqmspgkitzIGDq+yuvAI3wqFK0DbUdBkwN8SxbWoGCYs2cfPW09Ss0xBPnq4EfV9i7gwcKVUVtEEkdslNGavmQgnN0Oh8n8lCq+/hiBcfeA84+YGc+F6NMPuqs4wHZRIKbenCUJZjIGja61EcWIjFCpnDYPa9EnwygfAlZsxjF8Uyrydp6hbvjAf9W1I7bKp9yyrlMqZNEGovzMGjq2zEsXx9VCwjJUoAgb9mSiWh57l9fl7uHIrhpH31OTZ9lXx1NKEUm5HE4RK3tF11gh3x9ZBgdLQZgQEPAXe+bl4I5o3F4SweM8ZGlUsykd9G1KtVEFXR6yUykSaIFTqjm2wrno6GggFSkHr4dDsafAuwKLdp3nz1xBuRccxVoc4VcqtaIJQaXd8k5UojqyxuhlvMxyaPcP52x68Nm8Pv+87T/MqxfmwT0MqldAhTpXK6TRBqDt3YouVKA6vgvwloPWLmGbPMDfkCuMXhhJnDK92r0P/FpV0iFOlcjBNECr9Tm612ijCfod8xaH1MM7UGsDLi46w7tAF2lYvycQ+DahQNJ+rI1VKpYMmCJVx4UHWDXdhKyFfMUzLF5jt0Z3xK07iIcKbD/jzcFNfLU0olcO4bExq5UZ8A6D/HHhmFfg2R1b/m0fWd2djqyCalvXg5TnBPPN9EOevJj2IkVIq59EShEqfUztg7ftwcCnGpwg7y/djyMFmxHgV5p2edenRsLyWJpTKAbSKSTnP6V1WojiwmDjvwvzieT8TLnakTb3q/PvBepQomDfVXSilXEermJTzlG8E/WbCs+vwqNqeR2/OZFuB0dQ98Bl9Pl7MspAzro5QKZVOWoJQmevsHqtEsW8hNyUf38Z05UydQbzSqw1F8v9zlDullGtpFZPKemdDiF/7Pnn2/cp148MvHt2p0fMV2jas7erIlFIOtIpJZb2y9cjzyA/w/Cbiqt3Dk/HzaTyvPas/G8r1i1rtpFROoAlCOVcZf4oM+InYZzdwomR7OkTMJM+URoTPHgs3Lrg6OqVUCjRBqCzhXa4udV6cw/7eK9no2ZxyoVOJ/qguMUtfh+vnXR2eUioJmiBUlvJv0Iy2ryzg6/o/szgmgDxbviBuUn1Y/jpcO+fq8JRSDjRBqCzn4+XB0D73Un7QDzzhM4UF0c2I3/QF5pMGsOxVuHbW1SEqpdAEoVyoRdUSTB3Vj51N36XT7Q9YQWvMlq/hk4aw9BW4qo3ZSrmSXuaqsoV1hyJ4ZU4w3leP86nvH9S7sBTJ4wlNnrBGuStaydUhKuWWXHaZq4h0E5EDIhImIuOSWaeviOwVkVARmWnPayQim+x5wSLyiDPjVK7XrkYplo1qT7MmTXng5GMMKvgVF2s8BNu/hymN4ddhcPGIq8NUKldxWglCRDyAg0BnIBzYBvQzxux1WKcGMBvoZIy5JCKljTHnRaQmYIwxh0SkPLAdqGOMuZzc8bQE4T7+2HeOcfP2cOlGNONaF2QQC/HY+QPEx0D9h6HdS1CqlqvDVMotuKoE0RwIM8YcMcZEA7OAnonWGQx8boy5BGCMOW8/HzTGHLJfnwbOA6WcGKvKRu6uU4aVo9rzQMPy/Hv9Ne4L68H+R9dDy6GwbxF83gJmP2l166GUchpnJogKwEmH6XB7nqOaQE0R2SAim0WkW+KdiEhzwBs4nMSyISISJCJBERERmRi6crWi+b2Z9Egjpg0I4OKNaO77LoyPZAC3h+2CdqMh7A/4qi383A9ObXd1uEq5JVdfxeQJ1AA6Av2AqSJSNGGhiJQDfgQGGWPiE29sjPnGGBNgjAkoVUoLGO7oHv8yrBzVgQcbVeDTVWH0mH6A4FrDYdQe6PgaHN8IUzvBjw/Bic2uDlcpt+LMBHEKqOgw7WvPcxQOLDTGxBhjjmK1WdQAEJHCwGLgdWOM/ufnYkXye/FR34ZMHxjA5VvR9PpiIx8EnuN22zEwcg/c/S84swumd4UZ98ORteAmV+cp5UrOTBDbgBoiUkVEvIFHgYWJ1lmAVXpAREpiVTkdsdefD/xgjJnjxBhVDtKpdhlWjOpA7yYV+Hz1Ye6fsp7dEfFWldPIPdD1XbhwCH7oAd92gUMrNVEolQFOSxDGmFhgGLAc2AfMNsaEisg7ItLDXm05ECkie4HVwFhjTCTQF2gPDBSRXfajkbNiVTlHkXxevN+nITMGNeP67Vh6fbGB95buJ0p8oNVQGLEb7vsIrp2Bn/rANx1h328Q/48aSqVUKvRGOZVjXY2KYcLifczadpJqpQrw4cMNaVypmLUwNhqCZ8G6j+HSUSjtD+3HgP+DkMfDpXErlZ3ogEHKra09GMGrc4M5ezWKwe2qMqpzTXy87CQQFwshc2Hdh3DhIJSoYd1HUf9h8PB0beBKZQOaIJTbuxYVw4Ql+/l56wmqlirAB30a0rRysb9WiI+DfQsh8EM4FwJFK1ttFw0fA09v1wWulItpglC5xvpDF3hlbjCnr9zi6TZVeKlLLfJ5O1QpGQMHlkLg+3B6JxSuAG1GWn0+eeVzWdxKuYomCJWrXL8dy3tL9/HfzSeoUrIA7/dpQDO/4n9fyRg4/Aes/QBOboaCZaD1i9B0EOQt6JrAlXIBTRAqV9oYdoGX5wZz6vItBrWuwtiuiUoTYCWKY+sh8AM4uhbyFYdWL0DzIeBT2DWBK5WFNEGoXOvG7VgmLtvPD5uOU7lEft7v3YAWVUskvfLJrVaiOLQCfIpAi+esR/7iSa+vlBvQBKFyvU2HI3llbjAnLt5kYGs/Xu5Wi/zeyVzFdHqn1Zi9/zfwLgjNnoFWw6Cgduei3I8mCKWAm9GxvL/sADM2HqNS8fxM7N2AVtWSKU0AnAu1EkXofPD0gYBB0Ho4FC6XdUEr5WSaIJRysOVIJC/PDeZ45E2eaFmZcffWpkDeFO6JuHDIuuEu+H/WTXaNn4C2I3WUO+UWNEEolcit6Dg+WH6A7zYepULRfLzfuwGtq5dMeaOLR2H9JNg1EzDQ8FFoOxpKVMuSmJVyBk0QSiUj6NhFxs4J5uiFG/RvWYlx99ahYEqlCYAr4bDhE2s41PgYqNfH6sZDR7lTOVC6E4SI1AWKGGM2iogP8C+gHhAMvGeMueaMgNNDE4RKr1vRcXy88gDT1h+lfJF8TOzdgLY1UilNAFw7Cxs/haDpEHML/HtA+7FQtr7zg1Yqk2QkQWwFDhpj+ovIf4BxgAAGmGmMecIZAaeHJgiVUduPW6WJIxE36Ne8Eq91r00hH6/UN7wRCZs/hy3fQPQ1qHmvlSh8mzo/aKUyKCNjUtcA1tmvH8VKDJ2AqUDXTItQqWygaeXiLBnejmfbV+V/207QdVIggQfTMJRtgRJw91t/jXJ3YhNM6wQ/9oLjm5wfuFJOklqC8AY8RKQyUAUINsasAbYABZwcm1JZzsfLg1e712Hu863J5+3BgOlbeWVOMFejYlLfOF8x6PgKjAqBe96GM8HwXTf47j44vEoHL1I5TmoJYhcwAasUYfhrRLh6/HP4UKXcRuNKxVg8vB3PdajGL9tP0nVSIKsPnE/bxnkLQdtRMDLYGuUuMswqTUy9C/b+qoMXqRwjtTaIBsAMoBZWkugD3AZCgWXGmOFZEGOaaBuEcpZdJy8z9pfdHDp/nYeb+vLG/f4UyZeGtokEMVGw+2fryqdLR60xKdqOhPp9tatx5XJ6matSGXQ7No4pfxziq7VHKFnQm3cfqk+n2mXubCdxsbB3AayfDOf2QGFfaD0MmgwAb62xVa6R7kZqEeknIi/aryuKyCYRuSYiG0TE3xnBKpUd5fX0YGzX2iwY2oai+bx5akYQo2fv4srNNLRNJPDwhPp94Ll18Pgc607sZeNgUj1Y+z7cuuS8E1AqHVKrYtoLLDfGjBKR74EngMtYDdQbjDGdsiTKNNAShMoqt2Pj+HxVGJ+vOUyJAt78p1d9OvvfYWkiwYnNVjceh5ZbHQMGDIKWL2h/TyrLZOQy10rAbvv1fUAUUA14A9CLvFWulNfTg9FdavHrC20oXsCbwT8EMXLWTi7fjL7znVVqCY/Phuc2QM1usOlz+KQBLBwOkYczP3il7kBqCSIaqC4inYDiwCZjzCXgKtZVTUrlWvUqFGHhsLaMuLsGvwWf4Z6PA1keejZ9OytbD/p8Cy9uh0aPW43anwXAL4Osy2WVcoHUqphmY125lLDSs8aYaXZ1Uz1jTLYpRWgVk3Kl0NNXGPNLMPvOXKVHw/K83aMuxQtk4Aqla2dh8xewbbp1d3b1ztBuNFRunXlBK0XGqpiGAJOBZcDrdnLwAvICX6bhwN1E5ICIhInIuGTW6Ssie0UkVERmOsx/UkQO2Y8nUzuWUq5Ut3wRFg5rw6h7arI05AxdJq1lWciZ9O+wUFno/I51d3anN+D0DvjuXvi2KxxcrjfdqSzhtMtcRcQDOAh0BsKBbUA/Y8xeh3VqALOBTsaYSyJS2hhzXkSKA0FAAFbpZTvQ1K7eSpKWIFR2se/MVcbO2U3Iqavc36Ac43vUpUTBvBnbafRN2Pmj1TnglZNQpp51M57/g9bVUUqlU0ZKEIhIRxFZJSLn7ccfItIhDcdtDoQZY44YY6KBWUDPROsMBj5P+OI3xiTcqtoVWGmMuWgvWwl0S8MxlXK5OuUKM39oG8Z0qcny0LN0mRTIb8GnydCPMe/80OJZGL4THvwS4mJg7tPwWVO7N9mozDsBpWyp3QfRB/gD6AiUtB93AatE5KFU9l0BOOkwHW7Pc1QTqGnfV7FZRLrdwbZKZVteHnkY1qkGv73YjgrF8jFs5k6G/Lidc1cz+EXu4QWNHoOhm+GR/0K+4vDbKOvKpw2fwO1s0wO/cgOplSBex+pzqRtQ2H50w/ryfjMTju+J1WNsR6AfMFVEiqZ1YxEZIiJBIhIUEZGGXjeVymK1yhZi3vOtea17bQIPRnDPx2uZtfVExkoTAHnyQJ0HYPAqGPArlK4DK9+CSXVh1b/hxoXMOQGVq6WWIOoA7xpjVhhjrtuPFcBEoHYq254CKjpM+/LPDv7CgYXGmBhjzFGsNosaadwWY8w3xpgAY0xAqVKlUglHKdfw9MjDkPbVWD6yPf7lCjNu3h4en7aF45E3Mr5zEaja0UoSg1dBlfYQ+KF1d/bSV+DyyVR3oVRyUksQV4HGScxvDKRWlt0G1BCRKiLijTWexMJE6yzAKj0gIiWxqpyOAMuBLiJSTESKAV3seUrlWH4lC/Dz4JZM6FWfPeFX6Do5kGnrjhAXn0kXilRoalU7vbAF6j0E26bBlEYw/3mIOJA5x1C5Smr3QXwHDAA2AJvt2S2BNsAMY8zTKe5cpDvWZbIewHRjzH9E5B0gyBizUEQE+Air2ioO+I8xZpa97VPAa/au/mOM+S6lY+lVTConOXPlFm/MD+GP/edpWLEo7/duQK2yhTL3IJdPwqbPrLGzY6Og9n3WvRQVss3tSyobyMiQo8WB+UC7RIu2AN1Tuuw0q2mCUDmNMYZFwWd4e2Eo16JiGNqxOkPvqkZeT4/MPdCNC7DlK9j6DURdgSodrERRpYNVRaVytQx3921f1powEvserF/8Y40x2eYCbE0QKqe6eCOadxaFsmDXaWqWKcjE3g1oXKlY5h8o6ips/87q7+n6OSjfxEoUte6zGr1VrpTp40GIyLvAy8aYTP6pk36aIFROt2r/OV6fH8LZq1E81aYKL3WpSX5vJ/wGSzyAUcla9gBGD1uX0apcJUM3yimlskan2mVYMao9j7eoxLfrj9J1ciAbwpxwuaqXj9Wt+LAg6P2tlRQWPA+fNILNX1l3bStF6m0Qia86SlAbqKYlCKWcY8uRSMbN28PRCzd4JKAir91X586GOb0TxsChlbD+YzixCfKXgBbPQ/NnIJ8TqrpUtpKRRuqURlc3miCUcp6omDgm/36IqeuOUKKAN//3YD261i3r3IMe32QlikMrwLuQVdJo9YLVeaBySxlJEJVT2rEx5ngGY8s0miCUuwo5dYWX5wSz98xV7qtfjrd71KVUoQx2/peas3tg/SQInQ957O492oyA4lWce1yV5TK9kTo70gSh3FlMXDzfBB7hkz8Okc/Lgzfv96d3kwqIsy9TvXgENkyBXT9BfCzUfQjaDIdyDZ17XJVlNEEo5SbCzl9n3Nxggo5fol2NkkzoVZ+KxfM7/8DXzlqXxwZNh+jr1j0UbYZDtbv1XoocThOEUm4kPt7w3y3Hmbh0PwZ4uWstBrTyI0+eLPiivnUZts+wbry7dgZK+0PrF6FeH/DMwAh6ymU0QSjlhsIv3eT1+SGsPRhB08rFmNi7PtVLZ3J3HcmJjYaQudYARudDoVA5a7yKpoMgX9GsiUFlCk0QSrkpYwzzd57ind/2cvN2HMPvrs6zHarh5ZFFtzgZA4dXwcYpcGQNeBeEJk9Cy+egaKWsiUFliCYIpdxcxLXbvL0olMXBZ6hdthAf9GlIfd8iWRvEmWCrc8CQuVbiqNvLqn4q3yhr41B3RBOEUrnE8tCzvLkghAvXbzO4fVVG3VMTH68svl3pSrjVRhE0A6KvgV87aD0canTWBu1sSBOEUrnIlVsxvLtkH7O2naRKyQK8+1B9WlYtkfWBRF2BHT/A5i/h6ikoVdsqUdR/GDydfB+HSjNNEErlQhvCLvDqvD2cuHiTx1tUYty9tSnk44LO+OJiIGSe1aB9bg8ULGM1aAc8pV15ZAOaIJTKpW5Gx/LxioNM33CUMoV9+E+venSqXcY1wRhjNWRv/BQO/wFeBaDJE9DyeSjm55qYlCYIpXK7nScu8crcYA6eu07PRuV5635/ShR0YTXP2RCrQXvPL2Diwb+n1U5RoYnrYsqlNEEopYiOjeeLNWF8vjqMQj5e/OsBf3o0LO/87jpScuUUbP0agr6D21ehclurnaJGFx3EKItoglBK/enguWu8PCeYXScvc3ft0vy7Vz3KFcnn2qCirsLOH2HTF3A1HErWhFbDoMEj1vgVymk0QSil/iYu3jBj4zE+XH4AjzzCuHtr81jzSlnTXUeKgcXA3l+t0e7OBkOB0tBiCAQ8DfmLuzY2N6UJQimVpBORN3l1fjAbwiJpXqU4E3s3oErJAq4Oy2rQPhpoNWiHrQSv/NC4P7Qcql2OZzJNEEqpZBlj+CUonP9bvJfo2HhGda7JM22r4JlV3XWk5txeqyfZ4P+BiYM6D1gN2r5JfqepO6QJQimVqnNXo3hzQQgr9p6jXoXCvN+7If7lC7s6rL9cPWM1aG+bDrevQKXWVoN2zW7aoJ0BmiCUUmlijGFpyFne+jWEyzdjeK5DNYZ1qp713XWk5PY12Plfq0H7ygkoUd1q0G74KHi5uLE9B0opQTg17YpINxE5ICJhIjIuieUDRSRCRHbZj2cclr0vIqEisk9EpohLr8VTKncQEbrXL8fvozvQs1EFPlsdxn1T1rH9+EVXh/aXvIWsm+uG74Q+060eZH8bCZPqwZqJcCPS1RG6DaeVIETEAzgIdAbCgW1AP2PMXod1BgIBxphhibZtDXwAtLdnrQdeNcasSe54WoJQKvOtPRjBa/P2cPrKLZ5s5cfYrrUokNfT1WH9nTFwbL3VoH1oOXjmg8aPWw3aJaq5Orpsz1UliOZAmDHmiDEmGpgF9EzjtgbwAbyBvIAXcM4pUSqlktWhZimWj2rPk638+H7TMbpMCmT1gfOuDuvvRKBKO3h8NgzdAvX7WJ0EftoU/tcfTm51dYQ5ljMTRAXgpMN0uD0vsd4iEiwic0SkIoAxZhOwGjhjP5YbY/Yl3lBEhohIkIgERUREZP4ZKKUomNeTt3vUZc5zrcjn7cGg77bxwk87OHc1ytWh/VPp2tDzMxgZAu1Gw9F18G1n+LYL7PsN4uNcHWGO4uqm/0WAnzGmAbAS+B5ARKoDdQBfrKTSSUTaJd7YGPONMSbAGBNQqlSpLAxbqdynaeXiLBnejjFdarJy3znu+WgtP246Rlx8NrzQpVAZuPstGBUK975vjZ/9v8fhs2aw7VuIvunqCHMEZyaIU0BFh2lfe96fjDGRxpjb9uQ0oKn9uhew2Rhz3RhzHVgKtHJirEqpNPD2zMOwTjVYMbI9DSsW5c1fQ+n95Ub2nr7q6tCSlreg1bX4izvh4RngUwQWj4bJ9WD1BLimNdcpcWaC2AbUEJEqIuINPAosdFxBRMo5TPYAEqqRTgAdRMRTRLyADg7LlFIu5leyAD8+3ZzJjzTi5MWbPPDZeiYs2cfN6FhXh5Y0D09rCNTBq2DgEvBtDmsnwqS6MO9ZOL3T1RFmS069D0JEugOTAQ9gujHmPyLyDhBkjFkoIu9iJYZY4CLwvDFmv30F1BdYVzEZYJkxZnRKx9KrmJRyjcs3o5m4bD8/bz1JhaL5eKdnXe6u46IxJ+7EhTDrxrtdMyH6OlRsCS2fg9oPWAkll9Ab5ZRSTrft2EVem7eHQ+evc2+9svzrgbqULZIDemKNumLdeLfla7h8HAr7QvNnoMmTuaKDQE0QSqksER0bz9R1R5jyxyG8PPIwtmst+resjIere4lNi/g4OLgcNn8Bx9ZZ91M0fARaPAel67g6OqfRBKGUylLHI2/wxoIQ1h26QEPfIkx4qD51yxdxdVhpdzbEqn4Kng2xUVClg3XjnRsOZKQJQimV5YwxLNx9mv/7bS+XbsYwqLUfozrXzH53YqfkRiTsmAFbp8G101CsinVVVKPHwScbdWSYAZoglFIuc+VmDO8t28/PW09QvogP7/Ssxz3+OaAR21FcDOxbCJu/gvCt4F3I6s6j+ZAc352HJgillMsFHbvI6/NDOHDuGl3rluHtHnVdP9RpepzabiWK0PkQHws1u1rtFFU7Wt1+5DCaIJRS2UJMXDzT1h3lkz8O4iHCmK61GNDKL2c0Yid27ax1V3bQdLh5AUrVsaqfGjwC3vldHV2aaYJQSmUrJyJv8savIQQejKCBbxEm9KpPvQo5qBHbUUwUhMyFLV/C2T2Qr5h1iWzzwVDE19XRpUoThFIq2zHG8FvwGcYv2svFG7cZ2LoKo7vUpGBOasR2ZAwc32gliv2LAbGGR235PFRskW2rnzRBKKWyrSu3Ynh/2X5+2nKCckV8GN+jLl3qlnV1WBlz6Thsm2p1Ox51Bco1shJF3V7gmdfV0f2NJgilVLa3/fglXp+/h/1nr9HF32rELl80BzZiO4q+Abt/tu7SvnAQCpSGZk9D00FWj7PZgCYIpVSOEBMXz7frjzL5d6sRe3SXWjzZqjKeHjn85rT4eDiyyrr6KWwleHhD3Yesvp/KN3ZpaJoglFI5ysmLN3nz1xDWHIigXoXCTOhVnwa+RV0dVua4cMgqUeyaCTE3oFIr6+onF3USqAlCKZXjGGNYsucsby8KJfL6bQa08uOlLjUp5OPl6tAyx63LsOsnl3cSqAlCKZVjXY2K4YNlB/jvluOUKeTD2z3q0rVuGSSbXhV0x+Lj4OAy2PylSzoJ1AShlMrxdp64xKvzrEbse+qUYXzPulTI6Y3YiZ0NgS1fWZ0Ext227s5u8bxTOwnUBKGUcgsxcfF8t+Eok1YeQgRGd67JwNZ+Ob8RO7EbkbD9O9g2zRpPu3hVaP4sNHos0zsJ1AShlHIr4Zdu8tavoazafx7/coV596H6NKxY1NVhZb64GNj7q1WqCN9mdxLYH1oMsZJGJtAEoZRyO8YYloWc5V8LQ4m4fpsBLSszpmst92nETix8u3WXduh8q92iZjfrMtkqHTJ0l7YmCKWU27oaFcNHyw/ww+bjlC6Ul7cfqEu3emXdpxE7satnICihk8BIKO1vXSbb5Ml0JQpNEEopt7fr5GVem7eHvWeucnft0ozvWRffYjmnV9U7FhMFIXOsm+8KlIABv6ZrN5oglFK5QmxcPDM2HuOjFQcBGNW5BoPaVMHL3RqxHRlj9feUr2i6Nk8pQbjxu6aUym08PfLwTLuqrBzdnjbVSzBhyX56fLaBnScuuTo05xFJd3JIjSYIpZTb8S2Wn6kDAviqf1Mu3YjmoS838uaCEK5Gxbg6tBxFE4RSyi2JCN3qlWXl6PY82cqPn7Yc556P1rI4+AzuUrXubE5NECLSTUQOiEiYiIxLYvlAEYkQkV324xmHZZVEZIWI7BORvSLi58xYlVLuqZCPF2/3qMuCF9pQunBeXpi5g6dmbOPkxZuuDi3bc1qCEBEP4HPgXsAf6Cci/kms+j9jTCP7Mc1h/g/AB8aYOkBz4LyzYlVKub8GvkVZMLQNb97vz5ajF+k8aS2f/nGIqJg4V4eWbTmzBNEcCDPGHDHGRAOzgJ5p2dBOJJ7GmJUAxpjrxhhN90qpDPH0yMPTbavw++gO3FWrNB+tPEjXyYGs2n/O1aFlS85MEBWAkw7T4fa8xHqLSLCIzBGRiva8msBlEZknIjtF5AO7RPI3IjJERIJEJCgiIiLzz0Ap5ZbKF83Hl/2b8uPTzfHIIzw1I4hnvt/GiUj9HerI1Y3UiwA/Y0wDYCXwvT3fE2gHjAGaAVWBgYk3NsZ8Y4wJMMYElCpVKmsiVkq5jXY1SrFsRHtevbc2Gw9Hcs+ktXy88qBWO9mcmSBOARUdpn3teX8yxkQaY27bk9OApvbrcGCXXT0VCywAmjgxVqVULuXtmYdnO1Rj1Usd6Va3LFP+OMQ9H69leejZXH+1kzMTxDaghohUERFv4FFgoeMKIlLOYbIHsM9h26IiklAs6ATsdWKsSqlcrmwRH6b0a8zPg1uS39uDZ3/czsDvtnH0wg1Xh+YyTksQ9i//YcByrC/+2caYUBF5R0R62KsNF5FQEdkNDMeuRjLGxGFVL/0hInsAAaY6K1allErQqloJFg9vx5v3+7Pj+CW6Tgrk/WX7uRkd6+rQspz2xaSUUsk4fy2K95bsZ97OU5Qv4sMb9/tzr5v1FKt9MSmlVDqULuTDx4804pfnWlEkvzdDf9rBE99uJez8NVeHliU0QSilVCqa+RVn0bA2vNOzLsHhl+k2eR0Tluzj+m33rnbSBKGUUmng6ZGHAa38WDWmI72b+PJN4BHu/mgNv+465bZXO2mCUEqpO1CyYF4m9mnA/KGtKV3IhxGzdvHoN5s5cNb9qp00QSilVDo0rlSMBS+0YUKv+hw4d43uU9YxflGoW3UprglCKaXSySOP8FiLSqx+qSOPNqvIjI3H6PThGuZsDyc+PudXO2mCUEqpDCpWwJv/9KrPwhfa4lssP2N+2c3DX28i9PQVV4eWIZoglFIqk9T3LcK851vzfp8GHLtwgwc+Xc+bC0K4cjNnVjtpglBKqUyUJ4/QN6Aiq17qyAB7JLu7PlrDrK0ncly1kyYIpZRygiL5rZHsfnuxHdVKFWDcvD30+nIjweGXXR1ammmCUEopJ/IvX5jZz7Zi0iMNOX35Fj0/38Cr84K5eCPa1aGlShOEUko5mYjQq7Evq17qwNNtqjA7KJy7PlzDj5uPE5eNq500QSilVBYp5OPFG/f7s3REO/zLFebNBSH0/Hw9249fcnVoSdIEoZRSWaxmmULMHNyCT/s15sK1aHp/uZGxv+zmwvXbqW+chTRBKKWUC4gIDzQszx8vdeC5DtVYsOsUd324hhkbjhIbF+/q8ABNEEop5VIF8noy7t7aLBvZnkYVi/L2or3c/+l6th696OrQNEEopVR2UK1UQX54qjlf9W/CtahY+n69iZGzdnL+apTLYtIEoZRS2YSI0K1eOX4f3YEXO1VnyZ6zdPpoLdPWHSHGBdVOmiCUUiqbyeftwUtdarFiVHua+RXj34v30f2TdWw8fCFL49AEoZRS2ZRfyQJ8N6g50wYEEBUbx2NTt/DCzB2cuXIrS46vCUIppbK5e/zLsHJUB0bdU5Pf956j04dr+WJNGNGxzq120gShlFI5gI+XByPuqcHvozvQrkZJ3l92gG6TAwk8GOG0Y2qCUEqpHKRi8fx8MyCAGYOaYYAB07fywswdThkX26kJQkS6icgBEQkTkXFJLB8oIhEisst+PJNoeWERCReRz5wZp1JK5TQda5Vm2ch2jO1aiyolCiAimX4Mz0zfo01EPIDPgc5AOLBNRBYaY/YmWvV/xphhyezm/4BAZ8WolFI5WV5PD164q7rT9u/MEkRzIMwYc8QYEw3MAnqmdWMRaQqUAVY4KT6llFIpcGaCqACcdJgOt+cl1ltEgkVkjohUBBCRPMBHwBgnxqeUUioFrm6kXgT4GWMaACuB7+35Q4ElxpjwlDYWkSEiEiQiQRERzmvJV0qp3MhpbRDAKaCiw7SvPe9PxphIh8lpwPv261ZAOxEZChQEvEXkujFmXKLtvwG+AQgICMi+o24opVQO5MwEsQ2oISJVsBLDo8BjjiuISDljzBl7sgewD8AY87jDOgOBgMTJQSmllHM5LUEYY2JFZBiwHPAAphtjQkXkHSDIGLMQGC4iPYBY4CIw0FnxKKWUujPijJsrXCEgIMAEBQW5OgyllMpRRGS7MSYgqWWubqRWSimVTblNCUJEIoDjGdhFSSBr+9J1Dnc5D9Bzya7c5Vzc5TwgY+dS2RhTKqkFbpMgMkpEgpIrZuUk7nIeoOeSXbnLubjLeYDzzkWrmJRSSiVJE4RSSqkkaYL4yzeuDiCTuMt5gJ5LduUu5+Iu5wFOOhdtg1BKKZUkLUEopZRKkiYIpZRSScr1CSK1Ue9yChGZLiLnRSTE1bFklIhUFJHVIrJXREJFZISrY0oPEfERka0ists+j/GujimjRMRDRHaKyG+ujiUjROSYiOyxR7LM0V0wiEhRe7iE/SKyT0RaZdq+c3MbhD3q3UEcRr0D+iUx6l22JyLtgevAD8aYeq6OJyNEpBxQzhizQ0QKAduBB3Pa5yLWGJAFjDHXRcQLWA+MMMZsdnFo6SYio4EAoLAx5n5Xx5NeInIMqxPQHH+jnIh8D6wzxkwTEW8gvzHmcmbsO7eXIDI06l12YowJxOrwMMczxpwxxuywX1/D6uU3qcGmsjVjuW5PetmPHPuLTER8gfuwuuZX2YCIFAHaA98CGGOiMys5gCaItI56p1xERPyAxsAWF4eSLnaVzC7gPLDSGJMjz8M2GXgZiHdxHJnBACtEZLuIDHF1MBlQBYgAvrOr/qaJSIHM2nluTxAqGxORgsBcYKQx5qqr40kPY0ycMaYR1oBZzUUkR1b/icj9wHljzHZXx5JJ2hpjmgD3Ai/YVbQ5kSfQBPjSGNMYuAFkWltqbk8QqY56p1zDrrOfC/xkjJnn6ngyyi72rwa6uTiU9GoD9LDr7mcBnUTkv64NKf2MMafs5/PAfKzq5pwoHAh3KJnOwUoYmSK3J4g/R72zG3ceBRa6OKZcz27c/RbYZ4z52NXxpJeIlBKRovbrfFgXQ+x3aVDpZIx51Rjja4zxw/o/WWWM6e/isNJFRArYFz9gV8d0AXLk1X/GmLPASRGpZc+6G8i0izmcOeRotpfcqHcuDitdRORnoCNQUkTCgX8ZY751bVTp1gZ4Athj198DvGaMWeK6kNKlHPC9fbVcHmC2MSZHXx7qJsoA863fIXgCM40xy1wbUoa8CPxk/8g9AgzKrB3n6stclVJKJS+3VzEppZRKhiYIpZRSSdIEoZRSKkmaIJRSSiVJE4RSSqkkaYJQKgUi4iciJtHjsogMdJiOF5FTIvIf+x4ORKSEiHwnIhEictvuNfThRPu+R0TWi8hNEbkoIktFpKyIdLT3+5nDup/Z8zpm7TugcjNNEEqlzU6gn/14ymH+T8CTWB0lvgYk3Dw2DxiIdePlaKAEMFtEOoGVHLDuv2kIvAu8AQjWNfpKZQu5+kY5pe5ABPC7/ToG6GW/3mWM+dHuN+oLoIWInMTqYXOjMeZpABEJA5YBbwGrgH9h/UAbbIyZZe/rCxHJY28L4CMiJRNeO+/UlEqaliCUSpsuWEkiAvjVYX5+ESlvLwc4AdS3X29yWG+j/ZzQWV9T+/lvd/AaYxx7Sn3a4ZhPZyR4pdJDE4RSabMFqy+lzsBLDvPHY3Xw+CCwGfgyjftLSxcGvzoc89dU1lUq02mCUCptLhhjfrcfjl1ef4PVQVodoLU9wNEee1lLh/UShoFM6BQuYR9dHNbBrmJKEJ5wTKxeO5XKUtoGoVTalBeRRx2mveznQ8aYVY4rGmPWiEgg0F5EpmI1cL9iL37Hfh4PrACmikgNIBJ4AKuhW6lsQUsQSqVNY+Bnh0dqo3Y9BMzAasyeBFwG+iYkE2PMH1hjQ+zBuoJpIlbSOZf5oSuVPtqbq1JKqSRpCUIppVSSNEEopZRKkiYIpZRSSdIEoZRSKkmaIJRSSiVJE4RSSqkkaYJQSimVpP8HLTaWlGXRM1cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the loss v/s epoch for SGD model\n",
    "plt.title(\"Plotting SGD model's loss v/s epoch\", fontweight='bold')\n",
    "plt.plot(hist_sgd.history[\"loss\"])\n",
    "plt.plot(hist_sgd.history[\"val_loss\"])\n",
    "plt.legend([\"train\", \"test\"], loc=\"upper right\")\n",
    "plt.ylabel(\"LOSS\", fontweight='bold')\n",
    "plt.xlabel(\"EPOCH\", fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edf11c9f",
   "metadata": {},
   "source": [
    "The loss's value rises together with our learning rate, as was previously demonstrated. The lowest loss for the training dataset while employing the Adam optimizer with a learning rate of 0.0001 was 0.1553. This loss saw a sharp fall, which was then followed by a slow decline. The minimal loss for the training dataset for SGD with a learning rate of 0.001 was 0.5403, and then the loss gradually decreased over time. So, we can reach to this conclusion that, with a lower learning rate value, performance increases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "488d9853",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of user voted:\n",
      "movieId\n",
      "1         215\n",
      "2         110\n",
      "3          52\n",
      "4           7\n",
      "5          49\n",
      "         ... \n",
      "193581      1\n",
      "193583      1\n",
      "193585      1\n",
      "193587      1\n",
      "193609      1\n",
      "Name: rating, Length: 9724, dtype: int64\n",
      "\n",
      "Number of movies got voted:\n",
      "userId\n",
      "1       232\n",
      "2        29\n",
      "3        39\n",
      "4       216\n",
      "5        44\n",
      "       ... \n",
      "606    1115\n",
      "607     187\n",
      "608     831\n",
      "609      37\n",
      "610    1302\n",
      "Name: rating, Length: 610, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Creating a pivot table and filling na with 0s\n",
    "Df_final = Ratings.pivot(index='movieId',columns='userId',values='rating').fillna(0)\n",
    "#Getting number of users that voted by taking the aggregated count\n",
    "num_of_user_voted = Ratings.groupby('movieId')['rating'].agg('count')\n",
    "#Getting number of movies that got voted by taking the aggregated count\n",
    "num_of_movies_voted = Ratings.groupby('userId')['rating'].agg('count')\n",
    "print(\"Number of user voted:\")\n",
    "print (num_of_user_voted)\n",
    "print(\"\\nNumber of movies got voted:\")\n",
    "print (num_of_movies_voted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "15a6191c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tv\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting ratings v/s movieid\n",
    "sns.FacetGrid(Df_final, size = 6)\n",
    "v = sns.scatterplot(x = num_of_user_voted.index,y = num_of_user_voted, palette='pastel')\n",
    "v.axhline(y= 10, linewidth=4, color='greenyellow')\n",
    "plt.title(\"Plotting ratings v/s movieId\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "522d4c1c",
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>...</th>\n",
       "      <th>601</th>\n",
       "      <th>602</th>\n",
       "      <th>603</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>609</th>\n",
       "      <th>610</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174055</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176371</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177765</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179819</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187593</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2121 rows × 610 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    2    3    4    5    6    7    8    9    10   ...  601  602  603  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  0.0  4.0  0.0  4.5  0.0  0.0  0.0  ...  4.0  0.0  4.0   \n",
       "2        0.0  0.0  0.0  0.0  0.0  4.0  0.0  4.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "3        4.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5        0.0  0.0  0.0  0.0  0.0  5.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "6        4.0  0.0  0.0  0.0  0.0  4.0  0.0  0.0  0.0  0.0  ...  0.0  3.0  4.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  4.0  0.0  0.0   \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  4.0  0.0  0.0   \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  4.5  0.0  0.0   \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   604  605  606  607  608  609  610  \n",
       "movieId                                     \n",
       "1        3.0  4.0  2.5  4.0  2.5  3.0  5.0  \n",
       "2        5.0  3.5  0.0  0.0  2.0  0.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  2.0  0.0  0.0  \n",
       "5        3.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6        3.0  0.0  0.0  0.0  0.0  0.0  5.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2121 rows x 610 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From the above plot we are setting movie rating above threshold to 10\n",
    "Df_final = Df_final.loc[num_of_user_voted[num_of_user_voted > 10].index, :]\n",
    "Df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "1fc60534",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\tv\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\seaborn\\axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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nIlUAzgbwBoASpVSz/tZRACX632UAPrB87Yi+LNRy/99YLiJ1IlLX1tYW2w0gIqKUY3uAE5EcABsBfEUp1WF9TymlAKhY/I5Sap1SqlYpVTtx4sRYrJKIiFKYrQFORNKhBbdfKqV+py9u0aseof/fqi9vAjDV8vVyfVmo5URERCHZ2YtSADwGYK9Sao3lrT8AMHpC3gLgOcvyz+i9Kc8HcEqvyvwzgCUiUqh3LlmiLyMiIgrJzqcJfBjAzQB2i8gOfdk3ADwAYIOI3AagEcB1+nubAFwOoB5AD4BbAUApdVxE7gewTf/cd5VSx21MNxEROYBozWDOUltbq+rq6hKdDCIisl/Ih+9xJhMiInIkBjgiInIkBjgiInIkBjgiInIkBjgiInIkBjgiInIkBjgiInIkBjgiInIkBjgiInIkO6fqIiJKCV6vQkN7N1o6PCjJc6OqOBsuV8gJMihFMMAR0Zjm9Spsfuco7tiwA54BL9zpLqy5bgGWzp3MIJfiWEVJRGNaQ3u3GdwAwDPgxR0bdqChvTvBKaPRYoAjojGtpcNjBjeDZ8CL1k5PglJEscIAR0RjWkmeG+5031uhO92FSbnuBKWIYoUBjojGtKribKy5boEZ5Iw2uKri7ASnjEaLnUyIaExzuQRL507GrJWL0NrpwaRc9qJ0CgY4IhrzXC5B9cQcVE/MSXRSKIZYRUlERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI7EAEdERI5kW4ATkcdFpFVE9liWfUdEmkRkh/7vcst794pIvYi8JyIftyxfqi+rF5F77EovERE5i50luCcBLA2y/EdKqQX6v00AICJzANwAYK7+nf8WkTQRSQPwMIBPAJgD4Eb9s0RERGGNs2vFSqlXRKQqwo9fCeAZpVQfgPdFpB7Aufp79UqpQwAgIs/on3031uklIiJnSUQb3AoR2aVXYRbqy8oAfGD5zBF9WajlAURkuYjUiUhdW1ubHekmIqIUEu8A9wiAMwAsANAM4D9jtWKl1DqlVK1SqnbixImxWi0REaUo26oog1FKtRh/i8ijAF7QXzYBmGr5aLm+DGGWExERhRTXEpyIlFpefhKA0cPyDwBuEJHxIjINQA2ANwFsA1AjItNEJANaR5Q/xDPNRESUmmwrwYnIrwFcAmCCiBwBcB+AS0RkAQAFoAHA/wUApdQ7IrIBWueRQQC3K6WG9PWsAPBnAGkAHldKvWNXmomIyDlEKZXoNMRcbW2tqqurS3QyiIjIfhLqDc5kQkREjsQAR0REjhRRG5yI7IbWbmZ1CkAdgO8ppdpjnTAiIqLRiLSTyZ8ADAH4lf76BgBZAI5Cm5LripinjIiIaBQiDXAfVUottLzeLSJvKaUWisin7UgYERHRaETaBpcmIsbckBCRc6B12we0bv1ERERJJdIS3OcBPC4iOdC6ZHYA+LyIZAP4d7sSR0RENFIRBTil1DYA80QkX399yvL2BjsSRkRENBqR9qIcD+AaAFUAxolo4+qUUt+1LWVERESjEGkV5XPQhgVsB9BnX3KIiIhiI9IAV66UCvZ0biIioqQUaS/Kv4vIPFtTQkREFEORluAuAvBZEXkfWhWlAFBKqfm2pYzI4bxehYb2brR0eFCS50ZVcTZcrpDzxhJRlCINcJ+wNRVEY4zXq7D5naO4Y8MOeAa8cKe7sOa6BVg6dzKDHFGMhK2iFJE8/c/OEP+IaAQa2rvN4AYAngEv7tiwAw3t3QlOGZFzDFeC+xWAZdB6Tyr4PndHAai2KV1EjtbS4TGDm8Ez4EVrpwfVE3MSlCoiZwkb4JRSy/T/p8UnOURjQ0meG+50l0+Qc6e7MCnXncBUETlLRL0oRWRLJMuIKDJVxdlYc90CuNO1S9Bog6sqzk5wyoicI2wJTkTc0B6LM0FECnG6ijIPQJnNaSNyLJdLsHTuZMxauQitnR5MymUvSho74tWDeLg2uP8L4CsApkBrhzNS0AHgoZinhmgMcbkE1RNz2OZGY0o8exCHraJUSj2ot799TSlVrZSapv87SynFAEdERFGJZw/iSJ8m8GMRORPAHABuy/KfxzxFRETkWPHsQRzp0wTuA3AJtAC3CdrA79cAMMAREVHE4tmDONK5KD8FYDGAo0qpWwGcBSA/5qmhsLxehUNtXdh68BgOtXXB61WJThIRUVTi2YM40qm6PEopr4gM6rObtAKYGvPUUEic2omInCCePYiHLcGJ9nTTXSJSAOBRaL0p3wKwNeapoZA4tRMROYXRg/j86gmonphjWyZ92BKcUkqJyLlKqZMAfiIimwHkKaV22ZIiCopTOxERRSfSNri3ROQcAFBKNTC4xZ/RMGvFqZ2IiEKLNMCdB2CriBwUkV0isltEGOTiiFM7ERFFJ9JOJh+3NRU0LE7tRKmOD3ileIt0oHej3Qmh4XFqJ0pV7AVMiRBpFSUR0YixFzAlAgMcEdkuXC9gIrswwBGR7dgLmBKBAY6IbMdewJQIkfaiJCIaMfYCpkRggCOiuGAvYIo3VlESEZEjMcAREZEjMcAREZEjMcAREZEjMcAREZEjsRclERHFRbwn3GaAIyIi2yViwm1WURIRke0SMeE2AxwREdkuERNuM8AREZHtEjHhNgMcERHZLhETbrOTCRER2S4RE24zwBERUVzEe8JtBjiiJBXvMUNETsMARzRKdgSiRIwZInIadjIhGgUjEF2+9lXc+OgbuHztq9j8zlF4vWpU603EmCEip2GAIxoFuwJRIsYMETkNAxzRKNgViBIxZijevF6FQ21d2HrwGA61dY261EvkjwGOaBTsCkSJGDMUT3ZV7RJZiVLOO6Fqa2tVXV1dopNBY4CdnUGMzivxGjMUT4faunD52ld9Sr/udBc2rVwUty7k5BghLwrbelGKyOMAlgFoVUqdqS8rArAeQBWABgDXKaVOiIgAeBDA5QB6AHxWKfWW/p1bAHxTX+33lFJP2ZVmomjZOXg13mOG4ilc1a4Tt5cSw84qyicBLPVbdg+ALUqpGgBb9NcA8AkANfq/5QAeAcyAeB+A8wCcC+A+ESm0Mc1EUTMC0fnVE1A9MccxpSw7jYU2Rko82wKcUuoVAMf9Fl8JwCiBPQXgKsvynyvN6wAKRKQUwMcBvKiUOq6UOgHgRQQGTSJKMU5vY6TkEO+B3iVKqWb976MASvS/ywB8YPncEX1ZqOVElMISMS8hjT0Jm8lEKaVEJGY9XERkObTqTVRUVMRqtZREOHVVaKm4b5zcxkjJId4BrkVESpVSzXoVZKu+vAnAVMvnyvVlTQAu8Vv+t2ArVkqtA7AO0HpRxjbZlGicuio07hui4OI9Du4PAG7R/74FwHOW5Z8RzfkATulVmX8GsERECvXOJUv0ZTTGcOqq0LhviIKzLcCJyK8BbAUwU0SOiMhtAB4A8DEROQDgo/prANgE4BCAegCPAvgSACiljgO4H8A2/d939WU0xnDqqtC4b4iCs62KUil1Y4i3Fgf5rAJwe4j1PA7g8RgmjVKQ0a3cf2Awu5Vz31DyS1QbMafqopTAbuWn+c/hWFGYxX1DSSuR07Jxqi5KGU6euipSoTqULJldgsMnesb0vqHkFIdp2UKe6CzBUcrgjCGhO5QcPtEz5vcNJadEthEzwBGlEHYooVSTyGnZGOAoqfGZYb44hyOlmkS2n7MNjpIWBzAH4j6hVGRz+3nIFTHAUdLiM8OCY2cbIh/xfx4c0WjxmWHBcQ5HosgwwFHS4gDm0UvFSZiJYoWdTChpcXD36CRygC1RMmAbHCW1RLc3pXIJiG2YNEawDY5SUyLbm1K9xyLbMGmsYxUlUQip/hgajpmjsY4BjiiEVJ81hG2Y8ceJCZILqyiJQkj1Xpwul2Dp3MmYtXIRx8zFQapXacdLPNu1WYIjCsEJJSBOUB0/qV6lHQ/x7tnLEhxRCCwBUTTYqWd4oTIBs2zq2csARxQGZw2hSKV6lXY8xDsTwCpKIqIYcEKVtt3i3bOXA72JiGIk0RMTJDubOuLwaQJERJR4NmQCOJMJERElXjzbtdkGR0REjsQSHBER2SLRk5UzwBERUcwlw8wurKIkIqKYS4aZXRjgiIgo5pJhsnIGOCIiirlkeFwTA9wYx8d7EMXeWLuugm1vMszswk4mY1gyNAITOc1Yu67CbW+iJytnCW4MS4ZGYCKnGWvXVbjtTfTjmhjgRiHVqyGSoRGYyGnG2nWVzNvLKsoRckI1BB/vQRR7Y+26SubtZQluhJxQDZEMjcBETjPWrqtk3l4+TWCEth48hhsffSNg+TPLz8P51RNs/e1Y4uM9Ul+ip0OiQGPtukrw9vJpArGWzMXyaPCJ1anNCVXlTjTWrqtk3V5WUY5QMhfLaexwQlU5kV1Yghshl0sSPsaDKFwPtmTLTRPFGwPcKCRrsXyk2JaTepxSVe5kvK4ShwGOALAtJ1UZVeX+x41V5cmB11VisRclAQAOtXXh8rWvBpQENq1c5JgSqlONtR57oxHv0hSvq7hgL0oKj205qctpVeV2SURpitdVYrEXJQFIjkdbENkpET1OeV0lFgMcAYjNsIdUm5sz1dJLo5OIORM5nCixWEVJAEY/7CHVGtNTLb00enb2OA3VtsfhRInFTiYUE6nWmJ5q6aXRsytTw8xSwrGTCdkr1RrTUy29NHp2laZCte3NYmYp4RjgKCZSbcBxqqWXYsOOHqepmlkaCwPQ2ckkxpKt40K80pNqjempll5KXqnYU9KoVr187au48dE3cPnaV7H5naMJv1/FGtvgYijZ6uLjnZ5UG3Ccauml5JRs130kHNYGHXInM8DFULKdNMmWHiKnSrXMklOeZ6kLuaNZRRlDiRhnk0rpIXIqo23v/OoJqJ6Yk9TBDUjNatWRYICLoWQ7aZItPUSUHMZKGzSrKGMo2eriky09RJQ8Uq1aNQy2wcVLsp00yZaeZDMWukoTORwHesdLrMfZjOYGzJt3eCzhkpPweg/EAJfERnMD5s17eJyBgpyC13twCelkIiINIrJbRHaISJ2+rEhEXhSRA/r/hfpyEZG1IlIvIrtEZGEi0uwvHgOoR/N4j0Q8GiTVBOtlWpiVgbbOvqQZqE8UidFe78k2QUWsJLIEd6lS6pjl9T0AtiilHhCRe/TXdwP4BIAa/d95AB7R/0+YeOWWRjMFUKpOHxRP/tN1lea78ZkLKnHLE28yF0xJzb86cjTXu5NLf8k0TOBKAE/pfz8F4CrL8p8rzesACkSkNAHpM8WrdDSabv4cIjA8/67S19aW48EtB1jqpaQWbJqtwSE14uvdybU9iQpwCsBfRGS7iCzXl5UopZr1v48CKNH/LgPwgeW7R/RlCROvAdSjGasyVsa5jIYxu/ymlYvwzPLzsGBqAQfGU9ILFpC++dxurL5m/oiudydPCJGoKsqLlFJNIjIJwIsiss/6plJKiUhUlcB6oFwOABUVFbFLaRDxfHDiktkl2DSCx3vwQYuRsfZ6PdTWxScM2Iw9/UYvWEBqbO9FWYF7RPcKJz9ZIyEBTinVpP/fKiK/B3AugBYRKVVKNetVkK36x5sATLV8vVxf5r/OdQDWAdo4ODvTb5SO/OusR1s6ClcXPpJ2MzseDeJkdh3XsSZUEHNSW4+dgXq4dYcKSEXZ40d0vTv5vI/7QG8RyQbgUkp16n+/COC7ABYDaLd0MilSSn1dRP4JwAoAl0PrXLJWKXVuuN+Ix0BvOwZQc3LkxOPA+NEJF8Qa2rsdcX7bGagjWXesf9/rVTh8vBstHX3o6R9ERVE2pk1IqfM+qQZ6lwD4vYgYv/8rpdRmEdkGYIOI3AagEcB1+uc3QQtu9QB6ANwa/yQH4oMTnYml3tEJN7Ywlc9va6kqKyPNtvGTkYzNjGXzQ6hgOW1C6pfegAQEOKXUIQBnBVneDq0U579cAbg9DkkbkVhWVTi5LpxGJ1XarsIFsVQ9v/2DwMrF020L1JFmAmKVEbNrsoNkOV+TaZhAyhnJU3HDDagcaz0fnTq4NNaM8+zWJ9/Ea/XteHZHE/63/hgGB73DfznOwg1PSdXz2z8IeBVsG4IT7+E9I+lBOdx1m0xPC+dky6MQbZtZpPXrY6ENyEkdDux2qK0Ltz75Jq6vrcDalw6Y+2v1NfNxxfwpSbW/hjuuqXh++z8ctDTfjZvPr/Q5FvFsg4slO+5hCehLwKcJ2CHap+KyE8lp3BeR23rwGF6rb8djrx1Kif2VikEsnGDnamVxJtbecDZ6B4Zivo3x3H/RBtRIrtsEPC08qTqZOEa0bQqp3Mgea9wXkSvJcyPNhZTZX07rqBOsG/3dS2djXlmBLYEnnvsv2g4rkVy3ydTWygA3CtGOH0mmA59o8dgXydLQHU4kaawqzsY5lUU8dxLE6ZMmRBNQI7luKwqz8L2rzsQ3n91j3he/d9WZqCjMsiX94bCKcpSiqU6wVgcUZmXg2tpyzJiUi9mleak27mRUjHE3bx0+iW/8frctbQ2p0MYXTRoHB734455m3L1xV9JuD8WWcW9p7+5DRpoLPf1DCc+oRdoGd+uTb2LZ/DKIAEoBL+xqwhOfPZdtcLGQyCd6DyceN/dkFjTIl+Ri9uTYBvlUaOMbSQO/k9q2KDTjOlm9eW9A56JE3yuGOw+TqQ2OwwTizOUSeBXM4AY4a/bu4Vi7XDef8mDtlnp87Tc7IYK4PWooWUSbRqMq6fzqCaiemJPQ4BbrIR4cMuLLuE6WzS8zgxsQn3vFcMdiuPMwmZ5kwja4BEiWDhaJaKOK17anQntnKqQxGDumikr26uR4M64Tkfh2LorFsUimuS1ZgkuASHM4duZqEzUYM165u1QYVJyMaYzknIv188Oc/DyykbJeJ/EsDcXiWPg/hmrTykUJy6ywBJcA/jmcyuJM3H/lPLR0eMz3Adiaqx3JFD2xKPHFI3dnpHNibgbWLz8/KRrng0m23nmR5t5jXQpPlhqNZGJcJ6s378XKy2oC2uDsygTF6lgky1ARBrgEsN7Yjnf3oemkB8ufrvM5gWeW5No2oSsQ/Ykcq2oku2/qodJ53rTipApuhmS5EQCRZ3piXbWaqlW1djKvk8m5ON7dF7eMmtOOBasoE8S4sRVljze7fgOnbyqNx7t9TrLSfDduu6ga+1s6Y1JdGW1VYbCb3+rNe7G76WTUVah2dpZgddfIRdrpJdZVq8lYVZsMjOuktqoYZ00txAVn2N+5yGnHgiW4BAt1U8keP87MSdkx9120VYX+6SzNd+P62gpcv+71pOoYwOqukVclR5p7j3UpPNmqascypx0LBrgEC3VTKckdbwagqxeWB+0qPJrqymhPZP902pGmWCjJc6OyONMcZAoAz+9sClvFkgoznkRqNFXJ0WR6Yl21mkxVtWOdk44FA1yChbqpVBRlo6IoG7NWLsL+lk5bSiXRnMj+6UzWuRErCrPw5ctqIp4myGld1EfzfC+n5d6JGOCGYXfufribinFTSnTDr386M9PHYd0rgbPbJ7ox+vCJHjO4AdoN/pvP7sHCisKgN3i7HviYKKOtonVS7p2InUzCiMdYMWsADZVjTpaGX2vnkHll+UmRJn/Rzg6SCjOeRCOZZpFIlNGOH+WsKs7BElwYdufuI60eS8aqo2RMExB9N2endYtOplkkEmG0E5o7rco6FlK5jZqTLYdh96ShdkwIbMfJmEoneLQ3KCfe0OyclDkW54Kd55NxTRVmZYyo53EqTNIdTylyffCBpyNhd+4+1l3a7TgZU+QEN0VbskzWkuho2NWOFovHPdl9PhnX1Eh7+XKYia9Ub6NmG1wYdrd9xbq9xI5BzpGsM9naLKIdSB7N55NtW+PJOBeM0tG6Vw5hxa/fxj/9WGubHhz0xn0eS3/GNRVukuJIvm81kmvSKedJqrdRswQXht25+0jaS6KpzhnJ9FvDrXu4ddoxs3yyVocmojQb6/3hv76KwiwcPtET1fkVrHS0evNeDAx5h30gq90lJOOaeu9ox4hqX2LRhplqtR7hpHobNQPcMOzsNj1cAI32QonmZIx03cOtM5ZVGMl+Y4h3dY3dj6WpLM4MGDMYyfkVrHS0bH5Z0Cnn7J7H0p9xTc0pzUVlcTa+8fvdPtWpSmn7wc4q6+HOk2TOxPlL9U5LrKJMsHDVY9FW50RTpRrpuodbZyyrMKLZ3kRUAcW7usbOx9KU5rtx55JZAWMGIzm/0iTwES7hBv4HW4edw0tcLkHVhBxctaAMm1ctwl0fnxlQnRrufBntXKnhzpNEPaZqpJLp0TcjwRJcEou2Oiea3Gek6x5unbHMkUeaptGUbELlniPJVce7usaux9IYc5sebu8e0fllLR0Z+/+cyqKEzGMZjssl8CqY6TS2z+5OEuHOk1TstJHKg/9ZghsFu0sRI2nwjjT3Gc26w60zljnySNM00pJNqNzz4KA3olx1PEof1nMqK2NcTDshGfv36oXlWF93GGdMzBnR+WWUjqy5+guqiyPeN3Y+TcJfIjpJhDtPUr3TRqrhOLggIsnNx6O9yM7fiOW6YzXuKtI0hRufeG5VcchjF2qM0/rl55tPRbAuDzb2ye4xZqNpI4t0/fuOdmDIC7ywqwnX11b4jBVbfc18XDF/yqg6CBn7JpoOLHaxjou7emE5RIA0AT55dhmqJthXIgl1niTzOLtYtQ0moI0x5MoZ4PxEepON14kazQ012hPLzpv1SEWSplD7fvOqRXi3uTPksQsVGH/y6YX4wi/eClgeqwH9kQq2XZXFmVh7w9noHRiK+BiFOw+8XoXdTaewZV8L1m6pR2m+27zxKwVcOnMCaquKR7x+62eSocOQ16vw0nstONDShQe3xO5xU6NJTzLsF7vSlaDt40DvSFnH+hgX/ntHOzCnNNcnxzeS9pGR5Gwirf8eyYkVybrjnRuLJE0VhVn43lVnBjwxYHBIhW3fCNU2UpqfmRRdoYOdU43tvegdGAoZaIN1+//L3paQ54HLJZhXlo+O3gG4011oPuXBwy/XA9C2+ZqFZWHTGOl5lixtTS6XYFpxDlb86u2Ep8VITzJOLBCr4xVuPVXF2XHvPcoA56elwxN0mp/KYu3xNSPtXGH3eDGlEPMbSrLmNg+f6MGPXzqA2y6qNkseP37pAO67Ym7YTEeoLs+zS3Kx+pr5AWO4YtW2FmkmIRbn1Lqba4c9D1wuwQXVxSPa5khvhMk0I0hrZ/KkBUjOThvGfc/I1APAxu1Hht1H/ud2qON+vLsP+46Grl2xCwOcn5I8N66tDRzI+o3f78aCqQXmwY52fIjd48V++KmzYn4RJ0su3F9LhweN7b1mycNgfQq6wRogguWejRLPmhffw20XVSPNBdRWFuHC6mKfar1wVX7hglc0mYRYnFN1jccjOg/GjXPhivlTMK8sP6qSRKSBa7hgHc+agWjHhybzGDW70lea78ZnLqj0qcZdtbgGk/PCPyjY/9x+9ObaoPs6Pc2VkHsJA5yfquJszJiUO+xFHG1VQyxztMFubAdaO2NezWbHzCixEMlT0EMFCP/c86G2LvPz1qq6TZZBuaECFIBhg1e0mYQ5pbl46tZz0dM/iIqi7LBzPAY7Pl4V+bMDR1KSiDRYhAvW8a4ZiDTjkKw1FvFI35AXZnADtPP0mW2HcWF1MbYePBb0eg52bn/zud1BawZ6+ocSUopmgPPjcglml+ZFPKYn0huE3ePFNtQdwfc/Oc9nbNJoq9nsmBkllGiCY6gbVnmB9tTuSAMEMHwQDxeggOGrhUc7tm/ahNDHL9jxeX5nk88NprI4E/dfOQ8tHR5z343mZjhcsLAexzmlufjjlxehrSuwJ2E8c/ORZkaTtcbC8P6x2KQv2LXmX41bmu/G9bUVuP7R10Nez6HajMsK3Njkt68b2rvhTncF9GYNV0KMBQa4IKZNiP30NLGc8qYkz43K4kwsm19m1pc/v7MJCysKAk4sO29mVpHcHMINso4mOIaraowmQBj7MlwQDxeglAo9e4exzdb1Gz0W01xAZvo4nymjRnLzCnZ87l46G0tml2BeWT6Od/eh6aQHy5+ui1mOP1ywiPQ4JqJ9LpLMaCzSZVcthtersLe5IybpC3aMZpbk+lwHkTyNIdS1U5Q9PmBfVxVn46Gbzg7ozTpzcp5P34ZYY4ALwo6eTrFcZ0VhVsD4qO9ddSbKC7IwbpwrZjeJWM6MEu7mF2nPVf+0hapqNH47ktztcEF8uAAY7L3JeW4cautCS4cHpflurLluAVZv3usz5mzdK4d8qjpHcvMKd3yM73z6sTdjXiIJFSwiLQEl6wS+o02XnVWIDe3dMWmGCHWMNq9a5HMdhJt6bST9EBLVm5UBLgQ7ejrFap2HT/T4zCFYmJWBw8d78Ep9G6qKs0OOSxrJGDnj8+dWFYf9vHFzCFUFEe7mF2nP1XBGM2xjYm4G1i8/Hz39QwH7ZriL2P+9h246O2As3kM3nY21N5ztM5jcv6pzpDevcOdUJPskkk4ysX6axWhqM+xs5x1tLYudVZwtHR5sqDuClZfVmNdIZXEmvvPPZ0ZV/RzqGB3t8PhkljLTx2HdK4d8PltZnInM9DSfNrmlcydjzqpFaOnoQ3f/ICqLQu+rRPRmZYCzkV0Xo/UkNeYVDPfk4ng85Xq4KohwN79Ie66GE6thG+dN8w3kw5Vi/d9TCvinH7/qsx33v/Au7lwyM2xVp//Ny53uwvc/Oc/WNlRjH6zevBfL5pchzQWcU1mEC6qLMW6cy7anWYy0NmO4Dj+jvdZGk66G9m7sb+m07QZekufGiZ5+PP16I267qBq57jTkutPxxV9sj6q0GO4YuVxinm/t3X1Yfc18rHnxPSybX4Z8dxqKc91mJs34vSWzS8JOrhDpb9uFc1EOY6TzTdo5a7h1zsZQdeXWeRn9c5aFWRnYd7QDf9vfGrBN2kwXJ6Oe69GogvDviWV8L9w8k8P1XI1EtPNERjqfpTWTEu6GZ0wIZM2llua7cful0/G1JbNwsLUr5Pb737xWXDYdyy+uxocqC9DQ3j3iuU5D7ZOKwiwcauvCtobjZtXpY68dwtot9fg/T9fhj3uaze2O5jyI5hgYJc9o5qMMlZ7Dx7tjdq1Fmy7rdb7nHx0hj/FoGfv2RE8/Hn65Hj39Q7j/hXejukat6wl2jKzbcu1PXsfTW9/HysUz8Nhrh3DKM4R/DTJp9TvNpyI+R+Ixl6s/luDCsOYYrc+Uml2aN2zvvNF0uhiOtSol3JOLg/XkC1fiA2DOVThce5p/ugGg8Xjo2enPrSoOWf0TTc/VUGI9bMPrVTh8vBtvHT4Z0DPVyJ2GGwdkrXL9/KJqbNwevoRm7JuHX643qzXf+cfoBsYO1xnn84uqsWx+WUAG6e6NuzCvLD9gHxmdZPa3dAIIrBKze5aOUMespaMPqzfvNQf+A8DqzXsxa3Ku7b0frdd5sGMcqxu4/74dabf7UOdEQ3s32jr7fO5Z51VPNINaqPtM86nIqx0TMYsLA1wY1s4P1qBgdL1OT5OQj1zpHQh/Ao6mQdp6orR19eFnrx4KGxisVQPhekcBMG98oYJNsHQ/dNPZ6B9UYZ+iPNzJHWnP1XCZguHaOK3fNWbqD7eN+452+LRD+GdSwo0DOtjW5bOfrSU0EcAlwMKKgqiqOkfSnjNcZ5xwnQn8e4AOVxUe6hgYmQVrO81wGUR/Xq/C4JAKesz6h4bMDjxGRnTFpTVo7+qL+WTY/ueeNeg2n/KYx3h+WR5qSnJj+vvGvq0qzsbuplMjzhBaj5H1ev78omqf9fkHtWC/F8k0d8H2W7yGXTDAhWGcvNagYIwP8e96vWR2iU839VWLp4c98KGqDd3prpAdRaysJ/twgSGSEt/x7j50eAaHzYkGu6nvOnIK6145hMKsDPN7oZ6iHCoARZK7G+1z4Pxn6vefz9J/G/0veOM4tXX2hczE9A8qlBdkYvy408feuj9/99YRc78MeU/vF/99s/XgsVG154TKCFhvyBu3H8G3l80JeZ5az5tIuo0H++3SfDfq27oimuw4XOalob0b33xuN7760Rn41ZuNZpvhhyoKkTc+3TznIgnCIxFp9/rmUx68sKsJS+ZMitnYw2DpWL1574hKi8NN8RfsXAh3T5hbmjfsuMhEDp5ngAvDyMFag0KwC/3x1w5iYk6Gz4myoe4IVi2uCbiojQMfbUeRYPx7AfYPeZGR5kJP/xAa2rvNC2u4El9lcSaaTnpwqE1rJ7LmRNNcwOJZkzCvrCDgBmmmQ52urnj69UasWlyDvMx0s40g0u0ZrgQ2moGu/oG5sb0XP37pANYvPz9gpn7rNloveGM6o1ueeDNoJsZ4/18ee8OnFGzslzs+WoOiHDe++WzwKk+r0TTIh7upWNfbfMqDR/5Wj28tmxNwrIx9YZw3kXag8P/tlYunA0DYkvBwaTaOSf+gQmHmOCy/+Ayf9P7ntWcNWzsx2maBSLvXG4848u+MEauOMNZ0hLpGQxluij//IPb8ziYzE9h8yoP1dYex7ubagJqrcBnTRA+eZ4ALw8jBWqve/EtA88vycM3CCrzxvu8cgM2nPPj51kY8deu5UFABA2KtVWT+F2Ykpblonx0WrsR3/5XzsPzpOrMEtr7usE+vurml+ebvB7vx5mSk+dw0u/qGgnY2Ge3Ez9GMFYtkEthQM/Ub2+h/wV9bW+6zXf6ZGOv7/t890dOPWaX5Zsl/uP0SScncv6SkFNDa2YdBrzfkTcV/vftbu1CSNz7ojCPA6UwHEHoKMN+q3zSf3zb6eQx33IabhX5wSOHa2nIcOeUJCJb7WzoDMqLBfmc0pQnj/JlflofPX3wGevsGkTV+HE729Pt1r08LGA6yevNeTJuQib3NXSHbc63CBWH/KlFjerkLzwgcxhPJhOzW4SlGRmz5xdU4e2oBKouzUZ6fiZpJOWg+pZ1jc0vzMW6cb0ea0Q5VsRMDXBhG7mROaS4qi7Pxjd/vBgCzE8HVC8tx7rRCfPu5Pbhn6eyA3Py1teXo7h80gxQANBzrwluHT+LBLfvNG6D1wjRKc+vrDmPIC+z44KRP122D/w1h2fwyn7FxoW6ewXJcxknYfMqDzXuaA3LI1gvR/wZZWZyJssJMnxt9JINEoxXNQNdoJoENViKybqNxwc8oyUVWelrYTIy14T9Ye0y0F3u4eSn9O0B98SPVAIBj3f0oy88M+jvHu/sAIOS4vzMmRT8gvqIwK6DE5v/baTL8/JjDPen6m8/txopLa9B4vAeeAa/Pc+wyM9Lw/U/OQ6M+HVSo3xlNDUBJnhu1lfm4ZmEFvv7bnZbM4Zk4c0qBeYP3r1ouzXfjcxdOQ8OxXjO4hfvt4YJwpCX7SCdk95/i70RPP2ZNzsNHZkwCMPxcq/78g+qk3MQO6meAG4bLJaiakIOKomwsmFqA4919WFiRj+ZTfbj/hXdRUTgXy+aX4QFLnXhhVkbAzNw/vXkhTvYM4mBbl5kDNW6Asyafrse/emE51tcdHvZJy/43hHC5V6Ndyb+R13phGb+/aMakoN2PjQvRP0AaOdbCrAyzA8UMv3YJY/3DNTwDoatwgg109e+JaIhkEtjh5mgMFlwaLDdQ67Rb7vQ0zCvL93kf0ILcY68d8nkI7khvTv7Tjlm38eqF5fAMDGFIaVWBwToJVRZn4lhXP145cAxepQWdeeX5EVeThaqK8t/XxmTPxjVQPTEHfQODYavrgfBVssbTI1o7PUgTbVuur60waxpcAMoLM1E9IRsVxdlmzz//LvCjmeqqqjgbdy6ZhVuf3OZzXn3ruT2YUZKDs6YWBt2OqxeWo72nH+09/RFdn/4lYP/rL9LB6JFOyH6ipz/kFH/Rzg4U7Lz96c0L8b2rzsSPXzpg1gqdPbUA5fmZYfd3rDDARchaxfda/TEzCGSNH4c0l1bdZQ1Yd/12p1nKy3WnYXAIuHvjLp+OC0YVQ2m+28xFiSBs1+3h5oHzfz05zx1QlenfAzRYJxRrDhkAjnf3+TxJwT/Haq0usW5PsIswXE/McDnX4XoiGsJNArt51SK0d/Xj/faeoHM0AsFzrdMmnN5PoabdWjK7JOzNJ9KbUyQlDes2igClBVm4Sy9ZBOsQ8MDV87Hjg5Nm5sqdrj0OZfrEnJDToQW7BqxtyABw1K+b+MbtR3Dv0lkQAbr7h7DqmbdRmJWBWy+sxLqbP4Qhrwo6CfZw+8ad7sIvXj+ML1xcjXuWzsYD+jEwMoN3/Xan+fq2i6qR707DvPICKKXd7JUa+Wwxxraf6h0IGqSOnvLgrKnBtyPNdbqaNlimI2f8ODy7owkPbtmPZfPLUFEUvPRtBOFIu9oPNyG7tRPYkFfrxewftKKtcQh23m5vPIm/17cF1Ar5Z9jtwgAXpYb2bp9nbj36ykGs+ugMs3PGwy/X446PzfDp0XXbRdXYeeSk+Z1wuai2rj78b/3wPej8LyRrg7D1BjHkPV3vPr8sD9efG9gDdOncyT6dUJ7f2RRQgqyZlIOFlsmBDcECbbhcobEPQ/XEtAbXfUc7UFaQiXll+T7ba4wVM54gYMz7aATsUBMcu9PT8G5zJxrbu0O2EQLhnxCwdO5klBW4g067tWnlorA3n0h7ikZS0vDf7739gz4ZJyMjMHdKLmZNzkNbZ1/ANj+45QAWVhRGHOCCZUye+Ow5AaXWnoEh9A4Mmcez+ZQH3//Te3Cnu3xKs9b1hpsyzXrsf/LKIdy5ZIaZCbztomqf/z0DXvzurSP4wsXV2Hqo3SytzijJDVkDUJ6fiZ0fnNDbmTIxtzTPpznASF9RdkbQ82pCznif3rD+U169tK8Fz+5oCphma8WlNfjLuy14bsfp6y3cEB3DcJ2xAAStGjzR04+zpxbgz19ZhO2Nocd3GqJ9pl9je+A4WK/SxtP51wr5Z9jtwgAXpZYOj88zt3Y1deAXWxtw/5Vn4lvP7UFhVgZqSnJ8pp4aP86F/iFv0I4LZltG0emLuV9fPtxJHmzA5sKKQhzv7kO63pvSGHxdmu/GFy6ZHvTmPWfVIngVzM4KRqeTSE7IYDnvh246G0NemGOp/G/i4Xpi+vcoXffKITx009mYVpwTcAMM9QQBoyTlX9ICgOd2NGHFpTUBv290/z/VG7oqychBDzfINtzNZ7ib03BtjcZNxZhK6e6Nu7Bx+xHcf+XcoNWjf/zy6TF7wdLc0z94+hgM08MwWMZkX3NHwPk8IWe82VYWah9Zt+MfJz0Bzw87b1oxAJiZF+ujdzLTx+Gwvn6jxsFaRf+ZCyrRYwmw7nQXfnT9gqA1AOdMK8Bzu/6Bbz3nO3H5VWeVBUxXNmNSDu67Yi5+8j/1ISfO9h/y4fUqtHV54E5PwzPbDpu9Hi+oLsatT24LGGw/3GBx/45Foa6zNBewanENntl2usPY7NI8pKVh2PbAYOeY9dpWCtjW0B5w3Nbd/KGA8zZNABXBZBR2YYCLkHHQXSJ4fqdvbmzr+8dx/bkV+OOXF+FYVx++vnGnz0102oRs/PAv+8zvGB0Xpk/Mwdwp+T7VNS6X4ILq4qAPDQw1xiXNJej0DGBb43GU5rvR2tkfMB7PKBEFu7FbZ+uoLM7EvZ+YPewJGeq5X5Pz3Hi3udMcpGwNOodP9IQcZG30xPQfc/iZCyrR0qG1d1p7dlYUZuHwiZ6gAdsoSfmXtLxKq/49cqInoEPQZy6oxNc37jQ7C1knjc4bn4YsfZLZ0nx3yAHHsWg4D9fW6N+ho7I40+y2PaXA7TN3oLGfKou0Z+RVFmUHTXNFUfBq48riTDxw9Xy4RMzB2cGeGTY5PxMPWGYRUQpo7+oL27HEOpbrziWzzPPcegznrFoUco5DAOjoHYA73YXMdBfc6aenfvIMeFFeeLq61ljnA3/aa1bPWWeLOXLcYwY347PffHYPaiZp7WrWoL6rqQN4oxH3X3kmlj+93ec7qzfvRVmBO6D06XIJLptZgppJOfhQRSGOdfehrCAL3X2nS9zWTlnhBov7dyzyb+e3BtnmUx78affpDmNGlWRWehq2Hz4R8vquKs4OeY6V5p++tm+7qBqPvebbm3Vfc0dAW2txdgYqi4Ofe/HoaMIAF4FgJ5Y1N1ZbWYQL9V6OrZ2nG8SNg9ra4fFpH0hzAbMm52FK/vigPdfGjXPhivlTMK8sP+gzt6w536e3vo9PLpxqVgGsXDzdHHR99cJyvYfZmRgYUvjgRK9PB4DywixkZ6ThS796ywwo19dWYG9zR0AAuPXCSgx5FV7a14JpxdnYezT4zSdYLv/x1w6if9CLe353uoOHtTrV2hPTGDxtlOR6B4bwzLbDPjNVAMCJ3n7kjh8X9EJt6dACcbCSVporcGJjo3v/bRdV44HNe3Hv0lnoGdCGOhj76jo9UK5cPB3P+VU3GW0KoTIgxkwe7V398AwOoW/Ai8ribFQWZZlB31q9Gqqt0T+gN7b3YvnTddi0chEqi3NQlq8FM/+M0dK5k1FZlIUfXDMfX/d7z+i4Yj1upflu3P6RM3DoWDfWvXIQN5xTgZ6+IeRnpgd0oHjAr5TsTnfhv65fgHFpErJjSUN7t1m6rm/1HV9nVP19cKLXJz3+VdbnVRVhzbVn4VhXn1lSMY5Jjx48jIxLeWEWevoGUVGU6TMcQin4NDdYz6Hj3X041NYVkL5dTR14s+FEQJqvr60IOvbN61XY19KBfUc7zY4W+4524oLqYrNGx3+wfbDOSf7H6F/Oqwg7FKckz41LZk0yg5t12jjgdGbA/xmF/u1o1nPMaxlmYFx71n3s9XoxLs2F5RdXw6u083ZKYSYumBZdhj2WGOAi4H/TBoC7lszC+HQXqifk+JTAjHrrP+85im8tm4N1rxzElAItl2s8oDQzPc3s0nyorStoI3GoKY+MQHvbRdV4YVcT7l46O6AXm3FCr687jBvOqUDfoMLRU714fmcTvnvFHKSNc6G1ow93/Xan2emlNN+Ney+fja/rnWOsPUKNLuhvvH8cXgX09A3ha345ZOPi8q9+nF+Wh9svq8EXf/GWz0Xz45cOYP3/OR+9g0M+PTG/fcUcn5KctQrHf6aKRz69MGjOMCsjzedYGO9v3H4E375iTkAAKSvQGvbHj3Ohsb0XnX2DeOjlengGtB6KRqC7emE5yvIzfToUiQDZGWmYku/GG++3B1QVDQ568dd9LWjt8KC7//T4wFDjFq0dVaxtjRVF2Xjj/faQVatGDzxracgYT5nrTsPx7gGsefG9oB0w/KecWr5oGnIzM/DA5r343IXT0DOgHe/CrIyA4SD++0IpYEJOBhZWFGH6xBwsrChEjz5UxquAN95vR+/AkHlMrW1O1uppo8rRaDc2MnBGlXX/oELj8R6fY+NyAT/81FmYkJuByuJMM+13Wbr1W0s5Ww8e86lNMG72+e40nOwZxMMv78AXL6kJWu3mH+iDDTCf+5VF2NZwAoeP9/i0s3kGvPh7fRv+31Xz8K/P7g462P6nNy9EZ+8ANu9pNtsFjWNUmq9N0B0sY2DME1pRmGVOYO6fPqMGyr+39rpXDgUdSmCU7owH/Goly5yAfWx0JqqtLAzoTBQqw243USr6GbeT3fTaLPWDuhmJTgYREdnsauwIGSn5uBwiInIkBjgiInIkBjgiInIkR3YyKcAcXI26EX33UFsXnt3RBK8CfvbqIfzHp87CXb/daTbODyn49AxbtbgGHz6jGF9+5m1cX1sBz+AQflN3xOzk8bkLp6FvyIs1L+43Oxd854q5ePuDkz6fu++Kudj5wUn8VO8BecfHZuCu3+7Cisumm2lzCcx0eQa8uP3S6XhhVxPuXDILX9c7jGzcrq3zpX1HceN5lWjt6MMz2w7jSx+Zjv/acsCn80l5YRbea+k0v2MMmH3sNS0NX7i4Gj0DQ9iy9yiu+VAFOnv7UZg9Hg3tPeYMDQ+9VO/TOcBIw/1XzcU/TvRiYq4bX92wA1+6ZDr6h7QB6XcvnY19RzuQlZGG4uzxuOu3uwCcnoezoigL//GXfVhxaQ3u+u0un/V/+bLp+PFL9ZgxKQc3nluJ9u4+/PKNw/jWsjnYd7QDa7dos6msuGw6XKINP5halI29+nvWdVm7OhvLPYNDZi9U43OrFtegakI2vrp+h9kLzdhn6+u0304TwRd/+RY+v6ga2Rlp6B0YMjsWGPv7jIk5yM9MR1tnn9lp44Fr5uHbz+3B5y6chmz3OHztN9oxN/brt5bNwerNe3HP0tnYqz+fbsWl2r7077hg9KD1DHhxzydmwusFxrmAQS/MNEzKG2+eE8b5CmhTfH3pkulIT9OaM4zxU/PK8uBVCnds2Gmec4+9pk0H5hLte6sW1wCAObj784uq8bNXD/nsK+v5/NBL2jG6/dLpSHMBQ17ghV3atuSMT0NRznjsO9oJ9zgXfviX/ZhflodbPlyF1o4+n046D1w9H82nPPjG73f7nPvG/qitzMdnP1yND473wDMwhJ/q29g/5IV7nAueQa95Ld3ziZkoztbG7/3s1UO4Z+lMlORlYu/RDgAwz6s7PjYDa17cj9svnY6KokzcvXG3z7nrf/68tO8oPnNhNTz9g6gpycGp3kHsaTqF3gEvxrkABW37891pcKenobtfu96+eEkNdh45CUDrlGak39qzefU18/Ct5/Zg1eIZONjWjdmludjf0omaSadnUvrCxXpHqsJM9A8qfFXvkGbs++d2NJmd32aU5JqdzIxr/5lth7Hi0hqseXE/vrVsDt472oGfvnIIMybl4PMXnwGlvJic58bXN+7ymVkm2HSDa65bgMKsdNz46BsB991nlp8XMOl5LLAE58cYyG30NHrq74dw37K5SHMBHX1D+PlWrcfYisum47aLqvHzrY34xymP2SvMq04/3PLOJbPQ3tNvBjdA63X28MvaDBLGYPBl88twrLPP7L5+bW25OVYL0HptPb+zCbMn55npcqdrs6c3tvf6PMrEWGdd4yk0n/TgwS0H0Njeiw9O9prpWja/DN39QxDR1m3tVVhRmGn2vGrv6ceDW7R1rd1yADNL83DkxOngZvQm8++ldW1tOTz9Xnz/T/twoLULngFvQHDzKqC8IMtnO40xQINe5TNezbr+X7yudQe/ZNYk/NsL76CyOBvX1pab6zTWtXH7EUwtzEJpQRb2Wt6zrss6ONj4bWOiYuvnuvuH8MjL9Xog07bb2M+N7b3oG/DiQEsnVi2uwfM7mzC1MAtedbqHobG/dx45hd6BAfQPeeEZ0Gb42PnBKSybX4b2nn58cPz0vrCOXTTmOp1amAXPgBcTc8b7rN84H62TLHf1DWF93WHMLM0z09nVN4R//f0e/Hyrdm4a56sxyL5/yIveAe0cN2567x/rNjNyxjnnGfCa+9cYd/bglgPmeozBymmW88sYNmNsG6BNMeZV2tANs6ds9ngcOd6D53c24YyJOXCnu7BoxiQ0tvf4dItvbO/FrU9uM2fMWVQzwWdyBUCbQWN/Syce3HLAvLaqJ2bj+Z1NOKu8wOda6uobQrs+i8/Ky2rwxN8b0DswZO4fI83TJmSb+8HogQmc7klp7APjdV3jKaz89dv4+sbdePm9Nrx1+AQ8g15zuMrsyXlIcwH9QwrHuk9fbwdauszfLsrKMNNv/N6Jnn6c7B3AsvllaGzvRpoLWPc/B3FWeQGaT/Zg1WLtPtLe049j3f3o7fdir2UcrLHvtftRPR56qd68j1ivfeM6NK4xIx37W7uw8tdv4+6Nu3Gsqx93L52N9XWHzfMq2HSDd2zY4bPPDHaOiUuZACciS0XkPRGpF5F77Pqdkjy3OVXV+rrDOK96Ioa8Q/jw9AnmxWqcEA+/XI8TPf0ozc80B2saF/eJnn7sb+k0T3irusZTKMgch+mTcswbbVtXnxloyvIzzRPp+Z1NKMrKwA3nVOCx1w5i+cVnmOPpjEmaAZhjaoybDgB4Br0+XeSNdD38cj1+9uohuNNdmFeej1WLTy//x6le8wK2pt24GRsXpZGuVYtrfAaqGmkwBrJaZ3C5vrYCh9q6zAxE/5A36IU7KW+8z3i1YANhjZt508keM6BYb1jNpzxo7+pDb/+gz3v+TzqwXmzNpzw+22/93P7WLjz0Uj3c6WmoKM5CRdHp/dzlGcQTf29EVnoarlxQhuzxaWbwN6Zve+iler1k7EZrx+mbff/Q6fkKrcfcSKsRAKxjK7PGjwu6fiPtxnG4vrYCu4+c8rmpGYHVmiky1rVxu3Zsref46s3v4b9frkdlURaWX1xtPuCz+ZTHTE+PZdCy8d7Trzea+2rV4hpsqPvAZ9uMdBoZN+O49PQN4qmtjbjp3Eo89tpBLVPhQtDryDPgxVF9zOM5lUVmt3iD9Rw20jQ05DXH31nHpuaNT0NZYSZuOOf0JM5ZGWlm5tJIc9PJHjN4G4HE/3wJdv4AMAOWsc0nevrxyN/qcVZ5AfqHvD7b2NU/aGYQst3jfDKhKy6bjuUXV2NyntsnUO5v7cIvtjZgckEmsjPSUFaQaf5md9+gT6C2Hncr/2tfRFu/cY0FS8fcKXlYOncynvjsuchIk4DMo/V4DQwNYc11C3zuW3aOiUuJACciaQAeBvAJAHMA3Cgic+z4raribDM3YswIUVGcg7PLCsxg4H9w5pbm4ZzKIp+L2whAoU6ivMwMzC3NN9/bUPcBvvrRGTjR0x9Q2uodHMIZE3Pw1Y/NRGVRJtZefzYuml6MM6doT9M1LkD/nLLxW8DpwLD84mo88dlabFq5CJfNLMFlM0vwiTMn46lbz8UTn63FVQvKsOa6BebFZV1X/5DXvCiXX3wGHv/7+wCA2spCn99p7fTgRE+/edM00vb0642ompBtZiBaOzxBL5jyQjfOnlpgvlczKRehApE15229Ya1cPB3zyvNRVpDp8551XUbarMdzXnm+uf3+nzvR04/Vm9/D2i0HMLUwy3z/WLeWOfnJK4cw5NVy48XZGQHnyg+umY+5pXmYXpJjvrdx+xFtCiVLSXrZ/DK4XNp+NW6G7nSXWXptPtkTdP1z9KcrG+fh+rrD+JDl2FjPB+PvjduPmBkV49h+Sx94bHzmcxedgSVzJuOqBWUozR+P1dfMN9NjfM96rI3ff3DLAeRnpmPp3Mn4wTVnYX55HtZefzbOmVaI9cvPx0dmTMAdH5uJx17TSh7udJe5L5/8ewPOq56ITs8AaisLQ15HRs7f5RLMLs0L+Iz1e82nPDjQ1o31dYcxIddtXuMiwCnPEJ76+/tYVDMBP7jmLNRWFmBmSS7mleebQe+2i6pRXpiF9XWH4U5PQ3HueGRnpPkEfmvpNVhajHPRCN77W7vw4F/346zygoBzrigrA8XZGcjMcPlkQn/26iFkpqdhcv548zoxjtvW94/j3zftg2dgCBVFWT6laGug3rj9SMA5ZMxn63/tG/cV633GSMcZE3PMaQarJ+aYTxLxP9eM10XZ47F07mRsWrkIzyw/z5x1yK4xcSkxDk5ELgDwHaXUx/XX9wKAUurfg32+trZW1dWNrA0OOD0NVbBZRA4f70ZLR1/AM7oGB734457mgHnbAOBAS1fQKXUAmFMWWR//ke9OQ3GuO+CxH+EejmjMP9k/5DXniBtuOp9w23/4eDfe+UcHDh/vCTo42ZyNvCQXc0vzfGY2qSzOxNeWzETzKQ/WvLjfZ+byuVO0z67evBc3nFMR8snfxv68V5/53H87jMG+qy2Dka3z7hmzywDApj3N+E99Civ/fRvs6QoAcPh4d8AUZtbPWefBLMzKwK0frvJpZ/3akploPtmL0oIs9PYPYkphJs6vKjbnN7SeR9MmZGPf0U68f6w76DY+/tpBc7YaYzDt5IJMHA2yfpdLfM5d/3Qa+9H/71svrMSs0jwMeZU5KDvYA1D9r4/J+oM03/7gpO8s9SW5mD05L+CpAaGutY7efrx/rAf/tWU/bjq3Ej/66/6Ir6Ngz8izHocjJ3oDzuEfv3QgaBuR/7Xhf6yqLLP4WPebS4D27gHzurv1wkoUZo/3mePSes4a5+K88gIAwJQCN/Y2+54DRjtjVnoaPjjZa9YIuQSYX56Py2aWBFwn1n1fWZSFvx1oxT9O9ALQanR++UajeY1cUF2MktzxaOvqN+9nlUVZOHKyx7z2jbZaa9ua/zXmPzG19Z423P6NkZArTJUA9ykAS5VSn9df3wzgPKXUCstnlgNYDgAVFRUfamxsjHs6gwVGACGDovU71gmSjRvo4RM9Ixr5738DGvKGvlkNtx7/tBvTS4WaQsx6Yz1ysifodkeavuE+B8Dcd+70NHT3DYXdx9a0RbJvQ2V0ItnP0R6/UJkn6zaO9PwIlc7RnBvR7qto1uG/rZFcR6HSEew8NM7hYL8TSZrDZYCHO88AhNxP4TLQ4fbvcO8Z08QNKi8GhxT6B70h9591G420DAwNIXd8Ovq9XmREsL/CHUebSmrOD3BWoy3BERFRykj5mUyaAEy1vC7XlxEREQWVKgFuG4AaEZkmIhkAbgDwhwSniYiIklhKDPRWSg2KyAoAfwaQBuBxpdQ7CU4WERElsZQIcACglNoEYFOi00FERKkhVaooiYiIosIAR0REjsQAR0REjsQAR0REjsQAR0REjsQAR0REjsQAR0REjsQAR0REjsQAR0REjpQSTxOIloi0ARjt83ImADgWg+QkM6dvo9O3D+A2OoHTtw+wdxuPKaWWBnvDkQEuFkSkTilVm+h02Mnp2+j07QO4jU7g9O0DEreNrKIkIiJHYoAjIiJHYoALbV2iExAHTt9Gp28fwG10AqdvH5CgbWQbHBERORJLcERE5EgMcERE5EgMcEGIyFIReU9E6kXknkSnZyRE5HERaRWRPZZlRSLyoogc0P8v1JeLiKzVt3eXiCxMXMojJyJTReRlEXlXRN4RkVX6ckdsp4i4ReRNEdmpb9+/6cunicgb+nasF5EMffl4/XW9/n5VQjcgCiKSJiJvi8gL+mtHbaOINIjIbhHZISJ1+jJHnKcAICIFIvJbEdknIntF5IJk2D4GOD8ikgbgYQCfADAHwI0iMiexqRqRJwH4D368B8AWpVQNgC36a0Db1hr933IAj8QpjaM1COBOpdQcAOcDuF0/Vk7Zzj4AlymlzgKwAMBSETkfwGoAP1JKTQdwAsBt+udvA3BCX/4j/XOpYhWAvZbXTtzGS5VSCyzjwZxyngLAgwA2K6VmATgL2rFM/PYppfjP8g/ABQD+bHl9L4B7E52uEW5LFYA9ltfvASjV/y4F8J7+908B3Bjsc6n0D8BzAD7mxO0EkAXgLQDnQZsRYpy+3DxfAfwZwAX63+P0z0mi0x7BtpVDuwFeBuAFAOLAbWwAMMFvmSPOUwD5AN73Pw7JsH0swQUqA/CB5fURfZkTlCilmvW/jwIo0f9O+W3Wq6rOBvAGHLSdetXdDgCtAF4EcBDASaXUoP4R6zaY26e/fwpAcVwTPDL/BeDrALz662I4bxsVgL+IyHYRWa4vc8p5Og1AG4An9Grmn4lINpJg+xjgxiilZZ0cMUZERHIAbATwFaVUh/W9VN9OpdSQUmoBtFLOuQBmJTZFsSUiywC0KqW2JzotNrtIKbUQWvXc7SJysfXNFD9PxwFYCOARpdTZALpxujoSQOK2jwEuUBOAqZbX5foyJ2gRkVIA0P9v1Zen7DaLSDq04PZLpdTv9MWO206l1EkAL0OrrisQkXH6W9ZtMLdPfz8fQHt8Uxq1DwP4ZxFpAPAMtGrKB+GsbYRSqkn/vxXA76FlVpxynh4BcEQp9Yb++rfQAl7Ct48BLtA2ADV6L64MADcA+EOC0xQrfwBwi/73LdDarIzln9F7N50P4JSlaiFpiYgAeAzAXqXUGstbjthOEZkoIgX635nQ2hf3Qgt0n9I/5r99xnZ/CsBLes45aSml7lVKlSulqqBday8ppf4FDtpGEckWkVzjbwBLAOyBQ85TpdRRAB+IyEx90WIA7yIZti/RDZTJ+A/A5QD2Q2vv+NdEp2eE2/BrAM0ABqDlsG6D1laxBcABAH8FUKR/VqD1HD0IYDeA2kSnP8JtvAhatccuADv0f5c7ZTsBzAfwtr59ewB8W19eDeBNAPUAfgNgvL7crb+u19+vTvQ2RLm9lwB4wWnbqG/LTv3fO8Y9xSnnqZ7mBQDq9HP1WQCFybB9nKqLiIgciVWURETkSAxwRETkSAxwRETkSAxwRETkSAxwRETkSAxwRGOAiFSJ5ckSfu/9TURqg71HlMoY4IgczjIjCNGYwhOfKMnoE0e/oJQ6U3/9NQA5AI4D+AK0xwS9q5S6QZ8Z48cAzgSQDuA7SqnnROSzAK7Wv5eG0zNKGLOiPAHtsSb7AGTGZ8uI4osBjih13ANgmlKqz5jCC8C/Qpuu6nP6sjdF5K/6ewsBzFdKHfd7MOgXAfQopWaLyHxoj+EhchxWURKljl0Afikin4ZWigO0eQ3v0R+p8zdoU1lV6O+9qJQ6HmQ9FwP4BQAopXbp6yVyHAY4ouQzCN9r063//0/Q5vBbCGCb3rYmAK5R2pOiFyilKpRSxpOxu+OWYqIkxABHlHxaAEwSkWIRGQ9gGbRrdapS6mUAd0N7TEwOtCdcf1l/sgJE5OwI1v8KgJv0z58JbVJnIsdhGxxRklFKDYjId6HNlt8ErSNIGoBfiEg+tFLbWqXUSRG5H9oTsXeJiAvA+9ACYjiPQHv68l5oj99x+sNGaYzi0wSIiMiRWEVJRESOxABHRESOxABHRESOxABHRESOxABHRESOxABHRESOxABHRESO9P8BpWNowdgYXz0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting ratings v/s userid\n",
    "sns.FacetGrid(Df_final, size = 6)\n",
    "v = sns.scatterplot(x = num_of_movies_voted.index,y = num_of_movies_voted, palette='pastel')\n",
    "v.axhline(y= 50, linewidth=4, color =\"greenyellow\")\n",
    "plt.title(\"Plotting ratings v/s userId\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6bec39ce",
   "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>userId</th>\n",
       "      <th>1</th>\n",
       "      <th>4</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>...</th>\n",
       "      <th>600</th>\n",
       "      <th>601</th>\n",
       "      <th>602</th>\n",
       "      <th>603</th>\n",
       "      <th>604</th>\n",
       "      <th>605</th>\n",
       "      <th>606</th>\n",
       "      <th>607</th>\n",
       "      <th>608</th>\n",
       "      <th>610</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>movieId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174055</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176371</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177765</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179819</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187593</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2121 rows × 378 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "userId   1    4    6    7    10   11   15   16   17   18   ...  600  601  602  \\\n",
       "movieId                                                    ...                  \n",
       "1        4.0  0.0  0.0  4.5  0.0  0.0  2.5  0.0  4.5  3.5  ...  2.5  4.0  0.0   \n",
       "2        0.0  0.0  4.0  0.0  0.0  0.0  0.0  0.0  0.0  3.0  ...  4.0  0.0  4.0   \n",
       "3        4.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "5        0.0  0.0  5.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  2.5  0.0  0.0   \n",
       "6        4.0  0.0  4.0  0.0  0.0  5.0  0.0  0.0  0.0  4.0  ...  0.0  0.0  3.0   \n",
       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.0  0.0   \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  4.5  0.0   \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0   \n",
       "\n",
       "userId   603  604  605  606  607  608  610  \n",
       "movieId                                     \n",
       "1        4.0  3.0  4.0  2.5  4.0  2.5  5.0  \n",
       "2        0.0  5.0  3.5  0.0  0.0  2.0  0.0  \n",
       "3        0.0  0.0  0.0  0.0  0.0  2.0  0.0  \n",
       "5        0.0  3.0  0.0  0.0  0.0  0.0  0.0  \n",
       "6        4.0  3.0  0.0  0.0  0.0  0.0  5.0  \n",
       "...      ...  ...  ...  ...  ...  ...  ...  \n",
       "174055   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "176371   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "177765   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "179819   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "187593   0.0  0.0  0.0  0.0  0.0  0.0  0.0  \n",
       "\n",
       "[2121 rows x 378 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From the above plot we are setting user rating above threshold to 50\n",
    "Df_final = Df_final.loc[:, num_of_movies_voted[num_of_movies_voted > 50].index]\n",
    "Df_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6e4daeef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<2121x378 sparse matrix of type '<class 'numpy.float64'>'\n",
       "\twith 72893 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a csr matrix and removing sparsity\n",
    "final_df = csr_matrix(Df_final.values) \n",
    "Df_final.reset_index(inplace = True)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6fd947",
   "metadata": {},
   "source": [
    "In order to find the closest neighbors, we will be applying the cosine similarity technique, therefore we chose to use kNN for the movie recommendations for the afore mentioned movie names.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f56d7ee0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NearestNeighbors(metric='cosine', n_neighbors=10)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Creating a knn model with 10 neighbors\n",
    "neigh = NearestNeighbors(metric = 'cosine', n_neighbors = 10)\n",
    "neigh.fit(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "92bc1885",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a function for recommending the movie\n",
    "def Recommended_movie_for(name_of_movie):\n",
    "  \n",
    "    list_of_movie = movies_dataframe[movies_dataframe['title'].str.contains(name_of_movie)]\n",
    "    if len(list_of_movie):\n",
    "        id = list_of_movie.iloc[0]['movieId']\n",
    "        id = Df_final[Df_final['movieId'] == id].index[0]\n",
    "        d, i = neigh.kneighbors(final_df[id], n_neighbors = 10 +1)\n",
    "        movie_i = sorted(list(zip(i.squeeze().tolist(), d.squeeze().tolist())), key = lambda x :x[1])[:0:-1]\n",
    "        \n",
    "         \n",
    "        frame = []\n",
    "        for k in movie_i:\n",
    "            id = Df_final.iloc[k[0]]['movieId']\n",
    "            id = movies_dataframe[movies_dataframe['movieId'] == id].index\n",
    "            frame.append({'Title': movies_dataframe.iloc[id]['title'].values[0], 'distance': k[1]})\n",
    "            \n",
    "        final_dataset = pd.DataFrame(frame, index=range(1, 10 +1))\n",
    "        return final_dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "279901e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended movie for Apollo 13\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Speed (1994)</td>\n",
       "      <td>0.406600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>0.398175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Braveheart (1995)</td>\n",
       "      <td>0.396385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True Lies (1994)</td>\n",
       "      <td>0.392396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Terminator 2: Judgment Day (1991)</td>\n",
       "      <td>0.388963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>0.384633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fugitive, The (1993)</td>\n",
       "      <td>0.381795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Shawshank Redemption, The (1994)</td>\n",
       "      <td>0.380409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>0.348578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>0.343623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Title  distance\n",
       "1                        Speed (1994)  0.406600\n",
       "2                      Aladdin (1992)  0.398175\n",
       "3                   Braveheart (1995)  0.396385\n",
       "4                    True Lies (1994)  0.392396\n",
       "5   Terminator 2: Judgment Day (1991)  0.388963\n",
       "6                    Toy Story (1995)  0.384633\n",
       "7                Fugitive, The (1993)  0.381795\n",
       "8    Shawshank Redemption, The (1994)  0.380409\n",
       "9                 Forrest Gump (1994)  0.348578\n",
       "10               Jurassic Park (1993)  0.343623"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Recommended movie for Apollo 13')\n",
    "Recommended_movie_for('Apollo 13')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e33ff391",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended movie for Toy Story\n"
     ]
    },
    {
     "data": {
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       "      <td>Groundhog Day (1993)</td>\n",
       "      <td>0.399495</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lion King, The (1994)</td>\n",
       "      <td>0.398578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pulp Fiction (1994)</td>\n",
       "      <td>0.398293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Star Wars: Episode VI - Return of the Jedi (1983)</td>\n",
       "      <td>0.390663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Apollo 13 (1995)</td>\n",
       "      <td>0.384633</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Shrek (2001)</td>\n",
       "      <td>0.381472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n",
       "      <td>0.380789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Toy Story 2 (1999)</td>\n",
       "      <td>0.371637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>0.356542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Jurassic Park (1993)</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "                                                Title  distance\n",
       "1                                Groundhog Day (1993)  0.399495\n",
       "2                               Lion King, The (1994)  0.398578\n",
       "3                                 Pulp Fiction (1994)  0.398293\n",
       "4   Star Wars: Episode VI - Return of the Jedi (1983)  0.390663\n",
       "5                                    Apollo 13 (1995)  0.384633\n",
       "6                                        Shrek (2001)  0.381472\n",
       "7           Star Wars: Episode IV - A New Hope (1977)  0.380789\n",
       "8                                  Toy Story 2 (1999)  0.371637\n",
       "9                                 Forrest Gump (1994)  0.356542\n",
       "10                               Jurassic Park (1993)  0.334884"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Recommended movie for Toy Story')\n",
    "Recommended_movie_for('Toy Story')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "2de6c508",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recommended movie for Home Alone\n"
     ]
    },
    {
     "data": {
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       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True Lies (1994)</td>\n",
       "      <td>0.487959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Indiana Jones and the Temple of Doom (1984)</td>\n",
       "      <td>0.467394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mask, The (1994)</td>\n",
       "      <td>0.466844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pretty Woman (1990)</td>\n",
       "      <td>0.454946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Forrest Gump (1994)</td>\n",
       "      <td>0.454670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jurassic Park (1993)</td>\n",
       "      <td>0.454285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Aladdin (1992)</td>\n",
       "      <td>0.451129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jumanji (1995)</td>\n",
       "      <td>0.443432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Mrs. Doubtfire (1993)</td>\n",
       "      <td>0.405160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Lion King, The (1994)</td>\n",
       "      <td>0.403325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Title  distance\n",
       "1                              True Lies (1994)  0.487959\n",
       "2   Indiana Jones and the Temple of Doom (1984)  0.467394\n",
       "3                              Mask, The (1994)  0.466844\n",
       "4                           Pretty Woman (1990)  0.454946\n",
       "5                           Forrest Gump (1994)  0.454670\n",
       "6                          Jurassic Park (1993)  0.454285\n",
       "7                                Aladdin (1992)  0.451129\n",
       "8                                Jumanji (1995)  0.443432\n",
       "9                         Mrs. Doubtfire (1993)  0.405160\n",
       "10                        Lion King, The (1994)  0.403325"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Recommended movie for Home Alone')\n",
    "Recommended_movie_for('Home Alone')"
   ]
  }
 ],
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