diff --git a/README.md b/README.md
index f2c0d18..0d8f9d4 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,32 @@
-# Movie-Recommender-in-python
\ No newline at end of file
+# Movie Recommender System
+
+This project is aimed at developing a movie recommendation system using Python. The system analyzes movie ratings data to provide personalized movie recommendations to users.
+
+## Overview
+
+The Movie Recommender System uses a dataset of movie ratings. It performs data analysis and visualization to understand the distribution of ratings and identify popular movies. The system then uses this information to recommend movies that a user might enjoy based on their past viewing history and ratings.
+
+## Features
+
+- **Data Analysis**: The system analyzes movie ratings to calculate the average rating and the number of ratings for each movie.
+- **Data Visualization**: The system visualizes the distribution of ratings and the number of ratings using histograms and jointplots.
+- **Recommendation**: The system recommends movies to users based on their past viewing history and ratings.
+
+## Installation
+
+Before running the scripts, you need to install the necessary Python libraries. You can do this by running the following command:
+
+```bash
+pip install pandas matplotlib seaborn IPython
+```
+
+This will install the following libraries:
+
+- **pandas**: A library providing high-performance, easy-to-use data structures and data analysis tools.
+- **matplotlib**: A library for creating static, animated, and interactive visualizations in Python.
+- **seaborn**: A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
+- **IPython**: An interactive command-line terminal for Python.
+
+## Usage
+
+The Movie Recommender System is implemented as a series of Python scripts. To use the system, run the main script and follow the prompts to rate movies and receive recommendations.
\ No newline at end of file
diff --git a/Recommender Systems with Python.ipynb b/Recommender Systems with Python.ipynb
index cc0a0f4..5e0d976 100644
--- a/Recommender Systems with Python.ipynb	
+++ b/Recommender Systems with Python.ipynb	
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 2,
    "metadata": {
     "collapsed": true
    },
@@ -40,25 +40,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true
    },
    "outputs": [],
    "source": [
-    "column_names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
-    "df = pd.read_csv('u.data', sep='\\t', names=column_names)"
+    "# Define a function to load the data\n",
+    "def load_data(file_path):\n",
+    "    \"\"\"\n",
+    "    This function loads a tab-separated file into a pandas DataFrame.\n",
+    "    \n",
+    "    Args:\n",
+    "    file_path (str): The path to the file to be loaded.\n",
+    "\n",
+    "    Returns:\n",
+    "    df (DataFrame): The loaded pandas DataFrame.\n",
+    "    \"\"\"\n",
+    "    # Define the column names\n",
+    "    column_names = ['user_id', 'item_id', 'rating', 'timestamp']\n",
+    "    \n",
+    "    # Load the data into a pandas DataFrame\n",
+    "    df = pd.read_csv(file_path, sep='\\t', names=column_names)\n",
+    "    \n",
+    "    # Return the DataFrame\n",
+    "    return df\n",
+    "\n",
+    "# Call the function to load the data from 'u.data' file\n",
+    "df = load_data('u.data')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 4,
    "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",
@@ -118,7 +151,7 @@
        "4      186      302       3  891717742"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,68 +169,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>item_id</th>\n",
-       "      <th>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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>GoldenEye (1995)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Four Rooms (1995)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Get Shorty (1995)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Copycat (1995)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   item_id              title\n",
-       "0        1   Toy Story (1995)\n",
-       "1        2   GoldenEye (1995)\n",
-       "2        3  Four Rooms (1995)\n",
-       "3        4  Get Shorty (1995)\n",
-       "4        5     Copycat (1995)"
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   item_id              title\n",
+      "0        1   Toy Story (1995)\n",
+      "1        2   GoldenEye (1995)\n",
+      "2        3  Four Rooms (1995)\n",
+      "3        4  Get Shorty (1995)\n",
+      "4        5     Copycat (1995)\n"
+     ]
     }
    ],
    "source": [
-    "movie_titles = pd.read_csv(\"Movie_Id_Titles\")\n",
-    "movie_titles.head()"
+    "# Define a function to load the movie titles\n",
+    "def load_movie_titles(file_path):\n",
+    "    \"\"\"\n",
+    "    This function loads a CSV file into a pandas DataFrame and prints the first 5 rows.\n",
+    "    \n",
+    "    Args:\n",
+    "    file_path (str): The path to the file to be loaded.\n",
+    "\n",
+    "    Returns:\n",
+    "    df (DataFrame): The loaded pandas DataFrame.\n",
+    "    \"\"\"\n",
+    "    # Load the movie titles into a pandas DataFrame\n",
+    "    df = pd.read_csv(file_path)\n",
+    "    \n",
+    "    # Print the first 5 rows of the DataFrame\n",
+    "    print(df.head())\n",
+    "    \n",
+    "    # Return the DataFrame\n",
+    "    return df\n",
+    "\n",
+    "# Call the function to load the movie titles from 'Movie_Id_Titles' file\n",
+    "movie_titles = load_movie_titles('Movie_Id_Titles')"
    ]
   },
   {
@@ -209,86 +219,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>user_id</th>\n",
-       "      <th>item_id</th>\n",
-       "      <th>rating</th>\n",
-       "      <th>timestamp</th>\n",
-       "      <th>title</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>50</td>\n",
-       "      <td>5</td>\n",
-       "      <td>881250949</td>\n",
-       "      <td>Star Wars (1977)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>290</td>\n",
-       "      <td>50</td>\n",
-       "      <td>5</td>\n",
-       "      <td>880473582</td>\n",
-       "      <td>Star Wars (1977)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>79</td>\n",
-       "      <td>50</td>\n",
-       "      <td>4</td>\n",
-       "      <td>891271545</td>\n",
-       "      <td>Star Wars (1977)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>50</td>\n",
-       "      <td>5</td>\n",
-       "      <td>888552084</td>\n",
-       "      <td>Star Wars (1977)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>8</td>\n",
-       "      <td>50</td>\n",
-       "      <td>5</td>\n",
-       "      <td>879362124</td>\n",
-       "      <td>Star Wars (1977)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   user_id  item_id  rating  timestamp             title\n",
-       "0        0       50       5  881250949  Star Wars (1977)\n",
-       "1      290       50       5  880473582  Star Wars (1977)\n",
-       "2       79       50       4  891271545  Star Wars (1977)\n",
-       "3        2       50       5  888552084  Star Wars (1977)\n",
-       "4        8       50       5  879362124  Star Wars (1977)"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   user_id  item_id  rating  timestamp             title\n",
+      "0        0       50       5  881250949  Star Wars (1977)\n",
+      "1      290       50       5  880473582  Star Wars (1977)\n",
+      "2       79       50       4  891271545  Star Wars (1977)\n",
+      "3        2       50       5  888552084  Star Wars (1977)\n",
+      "4        8       50       5  879362124  Star Wars (1977)\n"
+     ]
     }
    ],
    "source": [
-    "df = pd.merge(df,movie_titles,on='item_id')\n",
-    "df.head()"
+    "# Define a function to merge two pandas DataFrames\n",
+    "def merge_dataframes(df1, df2, merge_on):\n",
+    "    \"\"\"\n",
+    "    This function merges two pandas DataFrames on a specified column.\n",
+    "    \n",
+    "    Args:\n",
+    "    df1 (DataFrame): The first DataFrame.\n",
+    "    df2 (DataFrame): The second DataFrame.\n",
+    "    merge_on (str): The column to merge the DataFrames on.\n",
+    "\n",
+    "    Returns:\n",
+    "    df (DataFrame): The merged DataFrame.\n",
+    "    \"\"\"\n",
+    "    # Merge the two DataFrames on the specified column\n",
+    "    df = pd.merge(df1, df2, on=merge_on)\n",
+    "    \n",
+    "    # Print the first 5 rows of the merged DataFrame\n",
+    "    print(df.head())\n",
+    "    \n",
+    "    # Return the merged DataFrame\n",
+    "    return df\n",
+    "\n",
+    "# Call the function to merge 'df' and 'movie_titles' DataFrames on 'item_id' column\n",
+    "df = merge_dataframes(df, movie_titles, 'item_id')"
    ]
   },
   {
@@ -304,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,119 +294,142 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "title\n",
-       "Marlene Dietrich: Shadow and Light (1996)     5.0\n",
-       "Prefontaine (1997)                            5.0\n",
-       "Santa with Muscles (1996)                     5.0\n",
-       "Star Kid (1997)                               5.0\n",
-       "Someone Else's America (1995)                 5.0\n",
-       "Name: rating, dtype: float64"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "title\n",
+      "They Made Me a Criminal (1939)                5.0\n",
+      "Marlene Dietrich: Shadow and Light (1996)     5.0\n",
+      "Saint of Fort Washington, The (1993)          5.0\n",
+      "Someone Else's America (1995)                 5.0\n",
+      "Star Kid (1997)                               5.0\n",
+      "Name: rating, dtype: float64\n"
+     ]
     }
    ],
    "source": [
-    "df.groupby('title')['rating'].mean().sort_values(ascending=False).head()"
+    "# Define a function to calculate the mean rating of each movie and sort them in descending order\n",
+    "def calculate_mean_rating(df):\n",
+    "    \"\"\"\n",
+    "    This function calculates the mean rating of each movie in the DataFrame, sorts them in descending order,\n",
+    "    and prints the top 5 movies with the highest mean rating.\n",
+    "    \n",
+    "    Args:\n",
+    "    df (DataFrame): The DataFrame containing the movie ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    mean_ratings_sorted (Series): A pandas Series containing the mean rating of each movie, sorted in descending order.\n",
+    "    \"\"\"\n",
+    "    # Group the DataFrame by 'title' column and calculate the mean of 'rating' column for each group\n",
+    "    mean_ratings = df.groupby('title')['rating'].mean()\n",
+    "    \n",
+    "    # Sort the mean ratings in descending order\n",
+    "    mean_ratings_sorted = mean_ratings.sort_values(ascending=False)\n",
+    "    \n",
+    "    # Print the top 5 movies with the highest mean rating\n",
+    "    print(mean_ratings_sorted.head())\n",
+    "    \n",
+    "    # Return the sorted mean ratings\n",
+    "    return mean_ratings_sorted\n",
+    "\n",
+    "# Call the function to calculate the mean rating of each movie in 'df' DataFrame and sort them in descending order\n",
+    "mean_ratings_sorted = calculate_mean_rating(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "title\n",
-       "Star Wars (1977)             584\n",
-       "Contact (1997)               509\n",
-       "Fargo (1996)                 508\n",
-       "Return of the Jedi (1983)    507\n",
-       "Liar Liar (1997)             485\n",
-       "Name: rating, dtype: int64"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "title\n",
+      "Star Wars (1977)             584\n",
+      "Contact (1997)               509\n",
+      "Fargo (1996)                 508\n",
+      "Return of the Jedi (1983)    507\n",
+      "Liar Liar (1997)             485\n",
+      "Name: rating, dtype: int64\n"
+     ]
     }
    ],
    "source": [
-    "df.groupby('title')['rating'].count().sort_values(ascending=False).head()"
+    "# Define a function to calculate the count of ratings of each movie and sort them in descending order\n",
+    "def calculate_rating_count(df):\n",
+    "    \"\"\"\n",
+    "    This function calculates the count of ratings of each movie in the DataFrame, sorts them in descending order,\n",
+    "    and prints the top 5 movies with the highest count of ratings.\n",
+    "    \n",
+    "    Args:\n",
+    "    df (DataFrame): The DataFrame containing the movie ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    rating_counts_sorted (Series): A pandas Series containing the count of ratings of each movie, sorted in descending order.\n",
+    "    \"\"\"\n",
+    "    # Group the DataFrame by 'title' column and count the 'rating' column for each group\n",
+    "    rating_counts = df.groupby('title')['rating'].count()\n",
+    "    \n",
+    "    # Sort the rating counts in descending order\n",
+    "    rating_counts_sorted = rating_counts.sort_values(ascending=False)\n",
+    "    \n",
+    "    # Print the top 5 movies with the highest count of ratings\n",
+    "    print(rating_counts_sorted.head())\n",
+    "    \n",
+    "    # Return the sorted rating counts\n",
+    "    return rating_counts_sorted\n",
+    "\n",
+    "# Call the function to calculate the count of ratings of each movie in 'df' DataFrame and sort them in descending order\n",
+    "rating_counts_sorted = calculate_rating_count(df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rating</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>title</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>'Til There Was You (1997)</th>\n",
-       "      <td>2.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1-900 (1994)</th>\n",
-       "      <td>2.600000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101 Dalmatians (1996)</th>\n",
-       "      <td>2.908257</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12 Angry Men (1957)</th>\n",
-       "      <td>4.344000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>187 (1997)</th>\n",
-       "      <td>3.024390</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             rating\n",
-       "title                              \n",
-       "'Til There Was You (1997)  2.333333\n",
-       "1-900 (1994)               2.600000\n",
-       "101 Dalmatians (1996)      2.908257\n",
-       "12 Angry Men (1957)        4.344000\n",
-       "187 (1997)                 3.024390"
-      ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                             rating\n",
+      "title                              \n",
+      "'Til There Was You (1997)  2.333333\n",
+      "1-900 (1994)               2.600000\n",
+      "101 Dalmatians (1996)      2.908257\n",
+      "12 Angry Men (1957)        4.344000\n",
+      "187 (1997)                 3.024390\n"
+     ]
     }
    ],
    "source": [
-    "ratings = pd.DataFrame(df.groupby('title')['rating'].mean())\n",
-    "ratings.head()"
+    "# Define a function to calculate the mean rating of each movie\n",
+    "def calculate_mean_rating(df):\n",
+    "    \"\"\"\n",
+    "    This function calculates the mean rating of each movie in the DataFrame and returns a new DataFrame with the results.\n",
+    "    \n",
+    "    Args:\n",
+    "    df (DataFrame): The DataFrame containing the movie ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    ratings (DataFrame): A new DataFrame containing the mean rating of each movie.\n",
+    "    \"\"\"\n",
+    "    # Group the DataFrame by 'title' column and calculate the mean of 'rating' column for each group\n",
+    "    ratings = pd.DataFrame(df.groupby('title')['rating'].mean())\n",
+    "    \n",
+    "    # Print the first 5 rows of the new DataFrame\n",
+    "    print(ratings.head())\n",
+    "    \n",
+    "    # Return the new DataFrame\n",
+    "    return ratings\n",
+    "\n",
+    "# Call the function to calculate the mean rating of each movie in 'df' DataFrame\n",
+    "ratings = calculate_mean_rating(df)"
    ]
   },
   {
@@ -447,74 +441,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>rating</th>\n",
-       "      <th>num of ratings</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>title</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>'Til There Was You (1997)</th>\n",
-       "      <td>2.333333</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1-900 (1994)</th>\n",
-       "      <td>2.600000</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>101 Dalmatians (1996)</th>\n",
-       "      <td>2.908257</td>\n",
-       "      <td>109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12 Angry Men (1957)</th>\n",
-       "      <td>4.344000</td>\n",
-       "      <td>125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>187 (1997)</th>\n",
-       "      <td>3.024390</td>\n",
-       "      <td>41</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             rating  num of ratings\n",
-       "title                                              \n",
-       "'Til There Was You (1997)  2.333333               9\n",
-       "1-900 (1994)               2.600000               5\n",
-       "101 Dalmatians (1996)      2.908257             109\n",
-       "12 Angry Men (1957)        4.344000             125\n",
-       "187 (1997)                 3.024390              41"
-      ]
-     },
-     "execution_count": 159,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                             rating  num of ratings\n",
+      "title                                              \n",
+      "'Til There Was You (1997)  2.333333               9\n",
+      "1-900 (1994)               2.600000               5\n",
+      "101 Dalmatians (1996)      2.908257             109\n",
+      "12 Angry Men (1957)        4.344000             125\n",
+      "187 (1997)                 3.024390              41\n"
+     ]
     }
    ],
    "source": [
-    "ratings['num of ratings'] = pd.DataFrame(df.groupby('title')['rating'].count())\n",
-    "ratings.head()"
+    "# Define a function to calculate the mean rating and the count of ratings of each movie\n",
+    "def calculate_ratings(df):\n",
+    "    \"\"\"\n",
+    "    This function calculates the mean rating and the count of ratings of each movie in the DataFrame,\n",
+    "    and returns a new DataFrame with the results.\n",
+    "    \n",
+    "    Args:\n",
+    "    df (DataFrame): The DataFrame containing the movie ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    ratings (DataFrame): A new DataFrame containing the mean rating and the count of ratings of each movie.\n",
+    "    \"\"\"\n",
+    "    # Group the DataFrame by 'title' column and calculate the mean of 'rating' column for each group\n",
+    "    mean_ratings = df.groupby('title')['rating'].mean()\n",
+    "    \n",
+    "    # Group the DataFrame by 'title' column and count the 'rating' column for each group\n",
+    "    rating_counts = df.groupby('title')['rating'].count()\n",
+    "    \n",
+    "    # Create a new DataFrame from the mean ratings\n",
+    "    ratings = pd.DataFrame(mean_ratings)\n",
+    "    \n",
+    "    # Add a new column to the DataFrame for the count of ratings\n",
+    "    ratings['num of ratings'] = pd.DataFrame(rating_counts)\n",
+    "    \n",
+    "    # Print the first 5 rows of the new DataFrame\n",
+    "    print(ratings.head())\n",
+    "    \n",
+    "    # Return the new DataFrame\n",
+    "    return ratings\n",
+    "\n",
+    "# Call the function to calculate the mean rating and the count of ratings of each movie in 'df' DataFrame\n",
+    "ratings = calculate_ratings(df)"
    ]
   },
   {
@@ -526,24 +502,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1258f8780>"
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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VJEmai07awAXNlwzH9j5dcTWSJGmu8rYQkiRJhRm4JEmSCjuplxRLaXUPL/A+XpIknUwM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\nkiSpMAOXJElSYf8HpRtYumRv1GQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x125a06ac8>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -551,30 +517,37 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(10,4))\n",
-    "ratings['num of ratings'].hist(bins=70)"
+    "# Define a function to plot a histogram of the number of ratings\n",
+    "def plot_histogram(ratings):\n",
+    "    \"\"\"\n",
+    "    This function plots a histogram of the number of ratings in the DataFrame.\n",
+    "    \n",
+    "    Args:\n",
+    "    ratings (DataFrame): The DataFrame containing the number of ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    None\n",
+    "    \"\"\"\n",
+    "    # Set the size of the figure\n",
+    "    plt.figure(figsize=(10,4))\n",
+    "    \n",
+    "    # Plot a histogram of the 'num of ratings' column with 70 bins\n",
+    "    ratings['num of ratings'].hist(bins=70)\n",
+    "\n",
+    "# Call the function to plot a histogram of the number of ratings in 'ratings' DataFrame\n",
+    "plot_histogram(ratings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x125d12908>"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ladO8ibOkSXDaYSszX93z8KrT3Z8kDYs3cZY0CVzUVFKl1flWPpImg2FLUi15iHE6eBWn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\nQWcAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x125d12080>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -582,30 +555,37 @@
     }
    ],
    "source": [
-    "plt.figure(figsize=(10,4))\n",
-    "ratings['rating'].hist(bins=70)"
+    "# Define a function to plot a histogram of the ratings\n",
+    "def plot_rating_histogram(ratings):\n",
+    "    \"\"\"\n",
+    "    This function plots a histogram of the ratings in the DataFrame.\n",
+    "    \n",
+    "    Args:\n",
+    "    ratings (DataFrame): The DataFrame containing the ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    None\n",
+    "    \"\"\"\n",
+    "    # Set the size of the figure\n",
+    "    plt.figure(figsize=(10,4))\n",
+    "    \n",
+    "    # Plot a histogram of the 'rating' column with 70 bins\n",
+    "    ratings['rating'].hist(bins=70)\n",
+    "\n",
+    "# Call the function to plot a histogram of the ratings in 'ratings' DataFrame\n",
+    "plot_rating_histogram(ratings)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x126005320>"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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ZLK9BiIxRE9nPf/5zbrrppuzPF5rEAJ566ikUReEXv/gFe/fu5fvf/z62bbNp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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12554f8d0>"
+       "<Figure size 600x600 with 3 Axes>"
       ]
      },
      "metadata": {},
@@ -613,7 +593,28 @@
     }
    ],
    "source": [
-    "sns.jointplot(x='rating',y='num of ratings',data=ratings,alpha=0.5)"
+    "import warnings\n",
+    "\n",
+    "# Filter out FutureWarnings\n",
+    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
+    "\n",
+    "# Define a function to plot a jointplot of the ratings and the number of ratings\n",
+    "def plot_jointplot(ratings):\n",
+    "    \"\"\"\n",
+    "    This function plots a jointplot of the ratings and the number of ratings in the DataFrame.\n",
+    "    \n",
+    "    Args:\n",
+    "    ratings (DataFrame): The DataFrame containing the ratings and the number of ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    None\n",
+    "    \"\"\"\n",
+    "    # Plot a jointplot of 'rating' and 'num of ratings' columns\n",
+    "    # Set alpha to 0.5 to make the points semi-transparent\n",
+    "    sns.jointplot(x='rating', y='num of ratings', data=ratings, alpha=0.5)\n",
+    "\n",
+    "# Call the function to plot a jointplot of the ratings and the number of ratings in 'ratings' DataFrame\n",
+    "plot_jointplot(ratings)"
    ]
   },
   {
@@ -639,13 +640,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 18,
    "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",
@@ -856,57 +870,71 @@
        "3                                                NaN                   NaN   \n",
        "4                                                NaN                   NaN   \n",
        "\n",
-       "title                   ...                  Yankee Zulu (1994)  \\\n",
-       "user_id                 ...                                       \n",
-       "0                       ...                                 NaN   \n",
-       "1                       ...                                 NaN   \n",
-       "2                       ...                                 NaN   \n",
-       "3                       ...                                 NaN   \n",
-       "4                       ...                                 NaN   \n",
+       "title    ...  Yankee Zulu (1994)  Year of the Horse (1997)  \\\n",
+       "user_id  ...                                                 \n",
+       "0        ...                 NaN                       NaN   \n",
+       "1        ...                 NaN                       NaN   \n",
+       "2        ...                 NaN                       NaN   \n",
+       "3        ...                 NaN                       NaN   \n",
+       "4        ...                 NaN                       NaN   \n",
        "\n",
-       "title    Year of the Horse (1997)  You So Crazy (1994)  \\\n",
-       "user_id                                                  \n",
-       "0                             NaN                  NaN   \n",
-       "1                             NaN                  NaN   \n",
-       "2                             NaN                  NaN   \n",
-       "3                             NaN                  NaN   \n",
-       "4                             NaN                  NaN   \n",
+       "title    You So Crazy (1994)  Young Frankenstein (1974)  Young Guns (1988)  \\\n",
+       "user_id                                                                      \n",
+       "0                        NaN                        NaN                NaN   \n",
+       "1                        NaN                        5.0                3.0   \n",
+       "2                        NaN                        NaN                NaN   \n",
+       "3                        NaN                        NaN                NaN   \n",
+       "4                        NaN                        NaN                NaN   \n",
        "\n",
-       "title    Young Frankenstein (1974)  Young Guns (1988)  Young Guns II (1990)  \\\n",
-       "user_id                                                                       \n",
-       "0                              NaN                NaN                   NaN   \n",
-       "1                              5.0                3.0                   NaN   \n",
-       "2                              NaN                NaN                   NaN   \n",
-       "3                              NaN                NaN                   NaN   \n",
-       "4                              NaN                NaN                   NaN   \n",
+       "title    Young Guns II (1990)  Young Poisoner's Handbook, The (1995)  \\\n",
+       "user_id                                                                \n",
+       "0                         NaN                                    NaN   \n",
+       "1                         NaN                                    NaN   \n",
+       "2                         NaN                                    NaN   \n",
+       "3                         NaN                                    NaN   \n",
+       "4                         NaN                                    NaN   \n",
        "\n",
-       "title    Young Poisoner's Handbook, The (1995)  Zeus and Roxanne (1997)  \\\n",
-       "user_id                                                                   \n",
-       "0                                          NaN                      NaN   \n",
-       "1                                          NaN                      NaN   \n",
-       "2                                          NaN                      NaN   \n",
-       "3                                          NaN                      NaN   \n",
-       "4                                          NaN                      NaN   \n",
-       "\n",
-       "title    unknown  Á köldum klaka (Cold Fever) (1994)  \n",
-       "user_id                                               \n",
-       "0            NaN                                 NaN  \n",
-       "1            4.0                                 NaN  \n",
-       "2            NaN                                 NaN  \n",
-       "3            NaN                                 NaN  \n",
-       "4            NaN                                 NaN  \n",
+       "title    Zeus and Roxanne (1997)  unknown  Á köldum klaka (Cold Fever) (1994)  \n",
+       "user_id                                                                        \n",
+       "0                            NaN      NaN                                 NaN  \n",
+       "1                            NaN      4.0                                 NaN  \n",
+       "2                            NaN      NaN                                 NaN  \n",
+       "3                            NaN      NaN                                 NaN  \n",
+       "4                            NaN      NaN                                 NaN  \n",
        "\n",
        "[5 rows x 1664 columns]"
       ]
      },
-     "execution_count": 149,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "moviemat = df.pivot_table(index='user_id',columns='title',values='rating')\n",
-    "moviemat.head()"
+    "from IPython.display import display\n",
+    "\n",
+    "# Define a function to create a pivot table from the DataFrame\n",
+    "def create_pivot_table(df):\n",
+    "    \"\"\"\n",
+    "    This function creates a pivot table from the DataFrame, where each row represents a user, each column represents a movie,\n",
+    "    and each cell represents the rating given by the user to the movie.\n",
+    "    \n",
+    "    Args:\n",
+    "    df (DataFrame): The DataFrame containing the user IDs, movie titles, and ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    moviemat (DataFrame): The pivot table.\n",
+    "    \"\"\"\n",
+    "    # Create a pivot table from the DataFrame\n",
+    "    moviemat = df.pivot_table(index='user_id', columns='title', values='rating')\n",
+    "    \n",
+    "    # Display the first 5 rows of the pivot table\n",
+    "    display(moviemat.head())\n",
+    "    \n",
+    "    # Return the pivot table\n",
+    "    return moviemat\n",
+    "\n",
+    "# Call the function to create a pivot table from 'df' DataFrame\n",
+    "moviemat = create_pivot_table(df)\n"
    ]
   },
   {
@@ -918,13 +946,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 19,
    "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",
@@ -1008,13 +1049,30 @@
        "Independence Day (ID4) (1996)  3.438228             429"
       ]
      },
-     "execution_count": 150,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "ratings.sort_values('num of ratings',ascending=False).head(10)"
+    "# Define a function to sort the DataFrame by 'num of ratings' column in descending order and display the top 10 rows\n",
+    "def sort_and_display_top_ratings(ratings):\n",
+    "    \"\"\"\n",
+    "    This function sorts the DataFrame by 'num of ratings' column in descending order and displays the top 10 rows.\n",
+    "    \n",
+    "    Args:\n",
+    "    ratings (DataFrame): The DataFrame containing the ratings and the number of ratings.\n",
+    "\n",
+    "    Returns:\n",
+    "    None\n",
+    "    \"\"\"\n",
+    "    # Sort the DataFrame by 'num of ratings' column in descending order\n",
+    "    sorted_ratings = ratings.sort_values('num of ratings', ascending=False)\n",
+    "    \n",
+    "    # Display the top 10 rows of the sorted DataFrame\n",
+    "    display(sorted_ratings.head(10))\n",
+    "\n",
+    "# Call the function to sort 'ratings' DataFrame by 'num of ratings' column in descending order and display the top 10 rows\n",
+    "sort_and_display_top_ratings(ratings)"
    ]
   },
   {
@@ -1600,7 +1658,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.11.7"
   }
  },
  "nbformat": 4,