diff --git a/Movie_Id_Titles b/Movie_Id_Titles.csv
similarity index 100%
rename from Movie_Id_Titles
rename to Movie_Id_Titles.csv
diff --git a/README.md b/README.md
index f2c0d18..df99bce 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,28 @@
-# Movie-Recommender-in-python
\ No newline at end of file
+# Movie Recommendation System
+
+## Overview
+This Movie Recommendation System is built in Python using content filtering techniques to recommend movies similar to those a user has already watched. It utilizes various libraries such as NumPy, Pandas, Matplotlib, Seaborn and FuzzyWuzzy, for data manipulation, analysis, and visualization.
+
+## Features
+- Recommends movies based on content filtering
+- Utilizes user ratings and movie metadata
+- Visualizes data using Matplotlib and Seaborn
+- Implements fuzzy matching for better movie title matching
+
+## Libraries Used
+- **NumPy**: For numerical operations and data manipulation.
+- **Pandas**: For handling and analyzing datasets.
+- **Matplotlib**: For plotting and visualizing data.
+- **Seaborn**: For enhanced data visualization.
+- **FuzzyWuzzy**: For string matching to improve movie title recommendations.
+
+## Datasets
+The program uses two datasets:
+1. **Ratings Dataset**: Contains columns `user_id`, `item_id`, `rating`, and `timestamp`. This dataset is used to track user preferences.
+2. **Movies Dataset**: Contains columns `item_id` and `title`. This dataset provides the titles of the movies to be recommended.
+
+## Installation
+To run this program, make sure you have Python installed, along with the required libraries. You can install the necessary libraries using pip:
+
+```bash
+pip install numpy pandas matplotlib seaborn fuzzywuzzy
diff --git a/Recommender Systems with Python.ipynb b/Recommender Systems with Python.ipynb
index cc0a0f4..6f0641e 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": 52,
    "metadata": {
     "collapsed": true
    },
@@ -40,25 +40,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 53,
    "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)"
+    "df = pd.read_csv('u.csv', sep='\\t', names=column_names)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 54,
    "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 +131,7 @@
        "4      186      302       3  891717742"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -136,13 +149,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 55,
    "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",
@@ -190,13 +216,13 @@
        "4        5     Copycat (1995)"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "movie_titles = pd.read_csv(\"Movie_Id_Titles\")\n",
+    "movie_titles = pd.read_csv(\"Movie_Id_Titles.csv\")\n",
     "movie_titles.head()"
    ]
   },
@@ -209,13 +235,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 56,
    "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",
@@ -281,7 +320,7 @@
        "4        8       50       5  879362124  Star Wars (1977)"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -323,22 +362,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "title\n",
+       "They Made Me a Criminal (1939)                5.0\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",
+       "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"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -349,7 +388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +403,7 @@
        "Name: rating, dtype: int64"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,43 +414,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>rating</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>title</th>\n",
-       "      <th></th>\n",
+       "      <th>rating</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>'Til There Was You (1997)</th>\n",
+       "      <th>0</th>\n",
+       "      <td>'Til There Was You (1997)</td>\n",
        "      <td>2.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1-900 (1994)</th>\n",
+       "      <th>1</th>\n",
+       "      <td>1-900 (1994)</td>\n",
        "      <td>2.600000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>101 Dalmatians (1996)</th>\n",
+       "      <th>2</th>\n",
+       "      <td>101 Dalmatians (1996)</td>\n",
        "      <td>2.908257</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12 Angry Men (1957)</th>\n",
+       "      <th>3</th>\n",
+       "      <td>12 Angry Men (1957)</td>\n",
        "      <td>4.344000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>187 (1997)</th>\n",
+       "      <th>4</th>\n",
+       "      <td>187 (1997)</td>\n",
        "      <td>3.024390</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -419,16 +473,15 @@
        "</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"
+       "                       title    rating\n",
+       "0  'Til There Was You (1997)  2.333333\n",
+       "1               1-900 (1994)  2.600000\n",
+       "2      101 Dalmatians (1996)  2.908257\n",
+       "3        12 Angry Men (1957)  4.344000\n",
+       "4                 187 (1997)  3.024390"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,13 +500,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 61,
    "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",
@@ -507,7 +573,7 @@
        "187 (1997)                 3.024390              41"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -526,24 +592,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1258f8780>"
+       "<Axes: >"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x125a06ac8>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -557,24 +623,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x125d12908>"
+       "<Axes: >"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x125d12080>"
+       "<Figure size 1000x400 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -588,24 +654,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.JointGrid at 0x126005320>"
+       "<seaborn.axisgrid.JointGrid at 0x18bddfb8>"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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ZLK9BiIxRE9nPf/5zbrrppuzPF5rEAJ566ikUReEXv/gFe/fu5fvf/z62bbNp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\nek/RsQXKgzjSc8xqkWRsskoiQ7JpF8cW7OovPecEaMPCxDREGxY9R8M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@@ -639,13 +705,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
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@@ -856,50 +935,42 @@
        "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,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -918,13 +989,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1008,7 +1092,7 @@
        "Independence Day (ID4) (1996)  3.438228             429"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1026,13 +1110,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
@@ -1086,7 +1183,7 @@
        "187 (1997)                 3.024390              41"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1104,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
@@ -1119,7 +1216,7 @@
        "Name: Star Wars (1977), dtype: float64"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1139,15 +1236,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/Users/marci/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:2487: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
-      "  warnings.warn(\"Degrees of freedom <= 0 for slice\", RuntimeWarning)\n"
+      "c:\\Users\\shukl\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\numpy\\lib\\function_base.py:2846: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  c = cov(x, y, rowvar, dtype=dtype)\n",
+      "c:\\Users\\shukl\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\numpy\\lib\\function_base.py:2705: RuntimeWarning: divide by zero encountered in divide\n",
+      "  c *= np.true_divide(1, fact)\n"
      ]
     }
    ],
@@ -1165,13 +1264,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 70,
    "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",
@@ -1218,7 +1330,7 @@
        "187 (1997)                    0.027398"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1238,13 +1350,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 71,
    "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",
@@ -1258,43 +1383,43 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>Commandments (1997)</th>\n",
+       "      <th>Hollow Reed (1996)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Cosi (1996)</th>\n",
+       "      <th>Stripes (1981)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>No Escape (1994)</th>\n",
+       "      <th>Star Wars (1977)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Stripes (1981)</th>\n",
+       "      <th>Man of the Year (1995)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Man of the Year (1995)</th>\n",
+       "      <th>Beans of Egypt, Maine, The (1994)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Hollow Reed (1996)</th>\n",
+       "      <th>Safe Passage (1994)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Beans of Egypt, Maine, The (1994)</th>\n",
+       "      <th>Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Good Man in Africa, A (1994)</th>\n",
+       "      <th>Outlaw, The (1943)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Old Lady Who Walked in the Sea, The (Vieille qui marchait dans la mer, La) (1991)</th>\n",
+       "      <th>Line King: Al Hirschfeld, The (1996)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>Outlaw, The (1943)</th>\n",
+       "      <th>Hurricane Streets (1998)</th>\n",
        "      <td>1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1304,19 +1429,19 @@
       "text/plain": [
        "                                                    Correlation\n",
        "title                                                          \n",
-       "Commandments (1997)                                         1.0\n",
-       "Cosi (1996)                                                 1.0\n",
-       "No Escape (1994)                                            1.0\n",
+       "Hollow Reed (1996)                                          1.0\n",
        "Stripes (1981)                                              1.0\n",
+       "Star Wars (1977)                                            1.0\n",
        "Man of the Year (1995)                                      1.0\n",
-       "Hollow Reed (1996)                                          1.0\n",
        "Beans of Egypt, Maine, The (1994)                           1.0\n",
-       "Good Man in Africa, A (1994)                                1.0\n",
+       "Safe Passage (1994)                                         1.0\n",
        "Old Lady Who Walked in the Sea, The (Vieille qu...          1.0\n",
-       "Outlaw, The (1943)                                          1.0"
+       "Outlaw, The (1943)                                          1.0\n",
+       "Line King: Al Hirschfeld, The (1996)                        1.0\n",
+       "Hurricane Streets (1998)                                    1.0"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1334,13 +1459,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 72,
    "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",
@@ -1394,7 +1532,7 @@
        "187 (1997)                    0.027398              41"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1413,13 +1551,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 73,
    "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",
@@ -1481,7 +1632,7 @@
        "Austin Powers: International Man of Mystery (1997)             130  "
       ]
      },
-     "execution_count": 157,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1499,13 +1650,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 74,
    "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",
@@ -1559,7 +1723,7 @@
        "Con Air (1997)            0.469828             137"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1574,14 +1738,460 @@
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": []
+   "source": [
+    "Taking user Input to get the movie and find the related results"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 75,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Top Gun (1986)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fuzzywuzzy import process\n",
+    "\n",
+    "movie_list = df['title'].unique()\n",
+    "input_movie = \"Top gun\"\n",
+    "\n",
+    "best_match, score = process.extractOne(input_movie, movie_list)\n",
+    "\n",
+    "print(best_match if score>80 else \"No movie Found\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "user_id\n",
+       "0    NaN\n",
+       "1    4.0\n",
+       "2    NaN\n",
+       "3    NaN\n",
+       "4    NaN\n",
+       "Name: Top Gun (1986), dtype: float64"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "users_movie_ratings = moviemat[best_match]\n",
+    "users_movie_ratings.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\shukl\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\numpy\\lib\\function_base.py:2846: RuntimeWarning: Degrees of freedom <= 0 for slice\n",
+      "  c = cov(x, y, rowvar, dtype=dtype)\n",
+      "c:\\Users\\shukl\\AppData\\Local\\Programs\\Python\\Python38-32\\lib\\site-packages\\numpy\\lib\\function_base.py:2705: RuntimeWarning: divide by zero encountered in divide\n",
+      "  c *= np.true_divide(1, fact)\n"
+     ]
+    }
+   ],
+   "source": [
+    "similar_to_users_movie = moviemat.corrwith(users_movie_ratings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Correlation</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>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101 Dalmatians (1996)</th>\n",
+       "      <td>0.492492</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12 Angry Men (1957)</th>\n",
+       "      <td>0.182154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187 (1997)</th>\n",
+       "      <td>-0.013104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 Days in the Valley (1996)</th>\n",
+       "      <td>0.112501</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Correlation\n",
+       "title                                   \n",
+       "'Til There Was You (1997)       0.333333\n",
+       "101 Dalmatians (1996)           0.492492\n",
+       "12 Angry Men (1957)             0.182154\n",
+       "187 (1997)                     -0.013104\n",
+       "2 Days in the Valley (1996)     0.112501"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_users_movie = pd.DataFrame(similar_to_users_movie, columns=['Correlation'])\n",
+    "corr_users_movie.dropna(inplace = True)\n",
+    "corr_users_movie.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Correlation</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>title</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Schizopolis (1996)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Blood For Dracula (Andy Warhol's Dracula) (1974)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>The Innocent (1994)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>American Dream (1990)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Selena (1997)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Dangerous Ground (1997)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fausto (1993)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Man of the Year (1995)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Run of the Country, The (1995)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bliss (1997)</th>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  Correlation\n",
+       "title                                                        \n",
+       "Schizopolis (1996)                                        1.0\n",
+       "Blood For Dracula (Andy Warhol's Dracula) (1974)          1.0\n",
+       "The Innocent (1994)                                       1.0\n",
+       "American Dream (1990)                                     1.0\n",
+       "Selena (1997)                                             1.0\n",
+       "Dangerous Ground (1997)                                   1.0\n",
+       "Fausto (1993)                                             1.0\n",
+       "Man of the Year (1995)                                    1.0\n",
+       "Run of the Country, The (1995)                            1.0\n",
+       "Bliss (1997)                                              1.0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_users_movie.sort_values('Correlation', ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Correlation</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>0.333333</td>\n",
+       "      <td>9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101 Dalmatians (1996)</th>\n",
+       "      <td>0.492492</td>\n",
+       "      <td>109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12 Angry Men (1957)</th>\n",
+       "      <td>0.182154</td>\n",
+       "      <td>125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>187 (1997)</th>\n",
+       "      <td>-0.013104</td>\n",
+       "      <td>41</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2 Days in the Valley (1996)</th>\n",
+       "      <td>0.112501</td>\n",
+       "      <td>93</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Correlation  num of ratings\n",
+       "title                                                   \n",
+       "'Til There Was You (1997)       0.333333               9\n",
+       "101 Dalmatians (1996)           0.492492             109\n",
+       "12 Angry Men (1957)             0.182154             125\n",
+       "187 (1997)                     -0.013104              41\n",
+       "2 Days in the Valley (1996)     0.112501              93"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_users_movie = corr_users_movie.join(ratings['num of ratings'])\n",
+    "corr_users_movie.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>Correlation</th>\n",
+       "      <th>num of ratings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Top Gun (1986)</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Pretty Woman (1990)</td>\n",
+       "      <td>0.614644</td>\n",
+       "      <td>164</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Michael (1996)</td>\n",
+       "      <td>0.579703</td>\n",
+       "      <td>119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Jungle2Jungle (1997)</td>\n",
+       "      <td>0.560635</td>\n",
+       "      <td>132</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Eraser (1996)</td>\n",
+       "      <td>0.536502</td>\n",
+       "      <td>206</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  title  Correlation  num of ratings\n",
+       "0        Top Gun (1986)     1.000000             220\n",
+       "1   Pretty Woman (1990)     0.614644             164\n",
+       "2        Michael (1996)     0.579703             119\n",
+       "3  Jungle2Jungle (1997)     0.560635             132\n",
+       "4         Eraser (1996)     0.536502             206"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_recommendations = corr_users_movie[corr_users_movie['num of ratings']>100].sort_values('Correlation',ascending=False)\n",
+    "best_recommendations.reset_index(inplace=True)\n",
+    "best_recommendations.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Here are the Best Recommendations for 'Top Gun (1986)'\n",
+      "1. Pretty Woman (1990)\n",
+      "2. Michael (1996)\n",
+      "3. Jungle2Jungle (1997)\n",
+      "4. Eraser (1996)\n",
+      "5. Firm, The (1993)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Getting only the movie namess\n",
+    "recommended_result = best_recommendations['title'].tolist()\n",
+    "\n",
+    "# recommended_result[1:12]\n",
+    "print(f\"Here are the Best Recommendations for '{recommended_result[0]}'\")\n",
+    "for i in range(1, 6):\n",
+    "    print(f\"{i}. {recommended_result[i]}\")"
+   ]
   }
  ],
  "metadata": {
@@ -1600,7 +2210,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.8.0"
   }
  },
  "nbformat": 4,
diff --git a/u.data b/u.csv
similarity index 100%
rename from u.data
rename to u.csv