IMDB_Rating_pred.ipynb

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      "cell_type": "markdown",
      "source": [
        "#MOVIE RATING PREDICTION WITH PYTHON"
      ],
      "metadata": {
        "id": "jaFAf9ecVd3O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Instructions:"
      ],
      "metadata": {
        "id": "f2JWItR-V1MD"
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      "cell_type": "markdown",
      "source": [
        "Build a model that predicts the rating of a movie based on features like genre, director, and actors. You can use regression techniques to tackle this problem. The goal is to analyze historical movie data and develop a model that accurately estimates the rating given to a movie by users or critics. Movie Rating Prediction project enables you to explore data analysis, preprocessing, feature engineering, and machine learning modeling techniques. It provides insights into the factors that influence movie ratings and allows you to build a model that can estimate the ratings of movies accurately."
      ],
      "metadata": {
        "id": "iqNWbCWsV2Ki"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Provide some trends:"
      ],
      "metadata": {
        "id": "2S2dEyvGWJrY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Year with best rating\n",
        "* Does length of movie have any impact with the rating?\n",
        "* Top 10 movies according to rating per year and overall.\n",
        "* Number of popular movies released each year.\n",
        "* Counting the number of votes which movies preformed better in rating per year and overall.\n",
        "* Any other trends or future prediction you may have\n",
        "* Which director directed the most movies\n",
        "* Which actor starred in the movie\n",
        "* Any other trends you can find"
      ],
      "metadata": {
        "id": "pineRnXkV5qN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Importing Important Libraries and Reading Dataset and Analyze"
      ],
      "metadata": {
        "id": "tHiDB7wsWY9s"
      }
    },
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              "                                 Name    Year Duration            Genre  \\\n",
              "0                                         NaN      NaN            Drama   \n",
              "1  #Gadhvi (He thought he was Gandhi)  (2019)  109 min            Drama   \n",
              "2                         #Homecoming  (2021)   90 min   Drama, Musical   \n",
              "3                             #Yaaram  (2019)  110 min  Comedy, Romance   \n",
              "4                   ...And Once Again  (2010)  105 min            Drama   \n",
              "\n",
              "   Rating Votes            Director       Actor 1             Actor 2  \\\n",
              "0     NaN   NaN       J.S. Randhawa      Manmauji              Birbal   \n",
              "1     7.0     8       Gaurav Bakshi  Rasika Dugal      Vivek Ghamande   \n",
              "2     NaN   NaN  Soumyajit Majumdar  Sayani Gupta   Plabita Borthakur   \n",
              "3     4.4    35          Ovais Khan       Prateik          Ishita Raj   \n",
              "4     NaN   NaN        Amol Palekar  Rajat Kapoor  Rituparna Sengupta   \n",
              "\n",
              "           Actor 3  \n",
              "0  Rajendra Bhatia  \n",
              "1    Arvind Jangid  \n",
              "2       Roy Angana  \n",
              "3  Siddhant Kapoor  \n",
              "4      Antara Mali  "
            ],
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              "      <th>Name</th>\n",
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              "      <th>1</th>\n",
              "      <td>#Gadhvi (He thought he was Gandhi)</td>\n",
              "      <td>(2019)</td>\n",
              "      <td>109 min</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.0</td>\n",
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              "      <td>Roy Angana</td>\n",
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              "      <th>3</th>\n",
              "      <td>#Yaaram</td>\n",
              "      <td>(2019)</td>\n",
              "      <td>110 min</td>\n",
              "      <td>Comedy, Romance</td>\n",
              "      <td>4.4</td>\n",
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              "      <td>Ovais Khan</td>\n",
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              "      <td>Ishita Raj</td>\n",
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              "      <th>4</th>\n",
              "      <td>...And Once Again</td>\n",
              "      <td>(2010)</td>\n",
              "      <td>105 min</td>\n",
              "      <td>Drama</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Amol Palekar</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15509,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13838,\n        \"samples\": [\n          \"Tumsa Nahin Dekha\",\n          \"Thanedaar\",\n          \"Farzande Hind\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 102,\n        \"samples\": [\n          \"(1974)\",\n          \"(1963)\",\n          \"(1971)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 182,\n        \"samples\": [\n          \"168 min\",\n          \"99 min\",\n          \"179 min\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 485,\n        \"samples\": [\n          \"Family, Mystery\",\n          \"Comedy, Fantasy, Musical\",\n          \"Action, Adventure, Crime\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3817771548659543,\n        \"min\": 1.1,\n        \"max\": 10.0,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          1.4,\n          7.0,\n          2.9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Votes\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2034,\n        \"samples\": [\n          \"837\",\n          \"101\",\n          \"2,566\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5938,\n        \"samples\": [\n          \"Param Hans Chitra\",\n          \"Vickram\",\n          \"Zaigham Ali Syed\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4718,\n        \"samples\": [\n          \"Abhishek Bachchan\",\n          \"Giri Babu\",\n          \"Ajay Bafna\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4891,\n        \"samples\": [\n          \"Bipasha Basu\",\n          \"Mukul Dev\",\n          \"Arjun Kapoor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4820,\n        \"samples\": [\n          \"Damandeep Singh\",\n          \"Kamini Kaushal\",\n          \"Babu\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "df = pd.read_csv('IMDb Movies India.csv', encoding='latin-1')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the Shape\n",
        "print('Dataset Shape:', df.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tH80ZHbsYDYh",
        "outputId": "3117d020-6b65-4881-b8e1-2fc4b86de8c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Shape: (15509, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the Dataset information\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ChmcQhFeYh2M",
        "outputId": "37e94e92-6bcc-42af-c49e-71782734134d",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 15509 entries, 0 to 15508\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Name      15509 non-null  object \n",
            " 1   Year      14981 non-null  object \n",
            " 2   Duration  7240 non-null   object \n",
            " 3   Genre     13632 non-null  object \n",
            " 4   Rating    7919 non-null   float64\n",
            " 5   Votes     7920 non-null   object \n",
            " 6   Director  14984 non-null  object \n",
            " 7   Actor 1   13892 non-null  object \n",
            " 8   Actor 2   13125 non-null  object \n",
            " 9   Actor 3   12365 non-null  object \n",
            "dtypes: float64(1), object(9)\n",
            "memory usage: 1.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the Null values\n",
        "df.isnull().sum()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
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        "outputId": "35e22785-36b8-43ef-be52-721110c0089e",
        "collapsed": true
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name           0\n",
              "Year         528\n",
              "Duration    8269\n",
              "Genre       1877\n",
              "Rating      7590\n",
              "Votes       7589\n",
              "Director     525\n",
              "Actor 1     1617\n",
              "Actor 2     2384\n",
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            ]
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting Duplicate values\n",
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0suBpgdesRE",
        "outputId": "735b0961-53f0-414d-9288-d4f322eb4fc0"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    {
      "cell_type": "code",
      "source": [
        "#Getting the EDA\n",
        "df.describe()"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 300
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        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Rating\n",
              "count  7919.000000\n",
              "mean      5.841621\n",
              "std       1.381777\n",
              "min       1.100000\n",
              "25%       4.900000\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2797.9713272009612,\n        \"min\": 1.1,\n        \"max\": 7919.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.841621416845562,\n          6.0,\n          7919.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Making a copy of the original data to prevent any inconvenience"
      ],
      "metadata": {
        "id": "_xvxFhlCdt1c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.copy()"
      ],
      "metadata": {
        "id": "nJ-ojOeQeMzb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Initiate Data Cleaning process...."
      ],
      "metadata": {
        "id": "8yxOf3M9eT87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Handling Null values\n",
        "df1.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "yijJSFbkek7V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.isnull().sum()"
      ],
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        "colab": {
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          "height": 398
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        "id": "8gdY6WayfFKV",
        "outputId": "981e198e-3faa-405a-c063-39a89c37591a",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name        0\n",
              "Year        0\n",
              "Duration    0\n",
              "Genre       0\n",
              "Rating      0\n",
              "Votes       0\n",
              "Director    0\n",
              "Actor 1     0\n",
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              "</div><br><label><b>dtype:</b> int64</label>"
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df1.info()"
      ],
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        "id": "7XFODUV3fNbl",
        "outputId": "b7ba08b9-f197-4aeb-b029-5f02e49d7db2",
        "collapsed": true
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5659 entries, 1 to 15508\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Name      5659 non-null   object \n",
            " 1   Year      5659 non-null   object \n",
            " 2   Duration  5659 non-null   object \n",
            " 3   Genre     5659 non-null   object \n",
            " 4   Rating    5659 non-null   float64\n",
            " 5   Votes     5659 non-null   object \n",
            " 6   Director  5659 non-null   object \n",
            " 7   Actor 1   5659 non-null   object \n",
            " 8   Actor 2   5659 non-null   object \n",
            " 9   Actor 3   5659 non-null   object \n",
            "dtypes: float64(1), object(9)\n",
            "memory usage: 486.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Handling the duplicates\n",
        "df1.drop_duplicates(inplace=True)\n",
        "df1.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHfP4N9_fZLW",
        "outputId": "d7f999b4-796f-4a04-965b-574696230c02",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5659 entries, 1 to 15508\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Name      5659 non-null   object \n",
            " 1   Year      5659 non-null   object \n",
            " 2   Duration  5659 non-null   object \n",
            " 3   Genre     5659 non-null   object \n",
            " 4   Rating    5659 non-null   float64\n",
            " 5   Votes     5659 non-null   object \n",
            " 6   Director  5659 non-null   object \n",
            " 7   Actor 1   5659 non-null   object \n",
            " 8   Actor 2   5659 non-null   object \n",
            " 9   Actor 3   5659 non-null   object \n",
            "dtypes: float64(1), object(9)\n",
            "memory usage: 486.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking duplicates\n",
        "df1.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StEKzz_cq5_1",
        "outputId": "34750148-7ab6-4984-82d1-12ea963204f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Handling the unwanted symbols in column \"Name\", \"Year\", \"Duration\" and Change the data-type of column \"Year\", \"Duration\" and \"votes\""
      ],
      "metadata": {
        "id": "s5OUVs79jBb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.head(5)"
      ],
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        "colab": {
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          "height": 310
        },
        "id": "NtKRGfAvmFCX",
        "outputId": "0ec7b562-849e-438d-d875-fac25d79263b",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 Name    Year Duration  \\\n",
              "1  #Gadhvi (He thought he was Gandhi)  (2019)  109 min   \n",
              "3                             #Yaaram  (2019)  110 min   \n",
              "5                ...Aur Pyaar Ho Gaya  (1997)  147 min   \n",
              "6                           ...Yahaan  (2005)  142 min   \n",
              "8                  ?: A Question Mark  (2012)   82 min   \n",
              "\n",
              "                       Genre  Rating  Votes        Director          Actor 1  \\\n",
              "1                      Drama     7.0      8   Gaurav Bakshi     Rasika Dugal   \n",
              "3            Comedy, Romance     4.4     35      Ovais Khan          Prateik   \n",
              "5     Comedy, Drama, Musical     4.7    827    Rahul Rawail       Bobby Deol   \n",
              "6        Drama, Romance, War     7.4  1,086  Shoojit Sircar  Jimmy Sheirgill   \n",
              "8  Horror, Mystery, Thriller     5.6    326   Allyson Patel        Yash Dave   \n",
              "\n",
              "                  Actor 2          Actor 3  \n",
              "1          Vivek Ghamande    Arvind Jangid  \n",
              "3              Ishita Raj  Siddhant Kapoor  \n",
              "5  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "6          Minissha Lamba   Yashpal Sharma  \n",
              "8          Muntazir Ahmad     Kiran Bhatia  "
            ],
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              "      <td>#Gadhvi (He thought he was Gandhi)</td>\n",
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              "      <td>109 min</td>\n",
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              "      <td>4.4</td>\n",
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              "      <td>?: A Question Mark</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 5659,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5385,\n        \"samples\": [\n          \"Railway Raju\",\n          \"Gambeeram\",\n          \"Hai Golmaal in White House\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 91,\n        \"samples\": [\n          \"(1990)\",\n          \"(1974)\",\n          \"(1970)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 174,\n        \"samples\": [\n          \"197 min\",\n          \"247 min\",\n          \"86 min\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 376,\n        \"samples\": [\n          \"Action, Romance, Drama\",\n          \"Comedy, Musical, Mystery\",\n          \"Musical, Comedy, Romance\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3811651416632527,\n        \"min\": 1.1,\n        \"max\": 10.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          8.4,\n          7.0,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Votes\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2027,\n        \"samples\": [\n          \"2,534\",\n          \"8,372\",\n          \"21,773\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2431,\n        \"samples\": [\n          \"Paresh Vinodray Savani\",\n          \"Ramesh Saigal\",\n          \"Omung Kumar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1960,\n        \"samples\": [\n          \"Mohan Kapur\",\n          \"Samir Aftab\",\n          \"Riya Deepsi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2321,\n        \"samples\": [\n          \"Arjun Fauzdar\",\n          \"Tara Deshpande\",\n          \"Saurabh Shukla\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2556,\n        \"samples\": [\n          \"Reema Sen\",\n          \"Nitya Shetty\",\n          \"Siddharth\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['Name'] = df1['Name'].str.replace('[^A-Za-z\\s\\'\\-]', '', regex=True)\n",
        "df1['Year'] = df1['Year'].str.replace(r'[()]', '', regex=True)\n",
        "df1['Duration'] = df1['Duration'].str.replace(' min', '', regex=False)\n",
        "df1['Votes'] = df1['Votes'].str.replace(',', '', regex=False)\n",
        "df1.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "QGdDmij8fmWZ",
        "outputId": "a0a20d2d-80a0-4c32-cefa-2d3f6e90ec57",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Name  Year Duration                      Genre  \\\n",
              "1  Gadhvi He thought he was Gandhi  2019      109                      Drama   \n",
              "3                           Yaaram  2019      110            Comedy, Romance   \n",
              "5                Aur Pyaar Ho Gaya  1997      147     Comedy, Drama, Musical   \n",
              "6                           Yahaan  2005      142        Drama, Romance, War   \n",
              "8                  A Question Mark  2012       82  Horror, Mystery, Thriller   \n",
              "\n",
              "   Rating Votes        Director          Actor 1                 Actor 2  \\\n",
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              "type": "dataframe",
              "variable_name": "df1",
              "repr_error": "0"
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          "metadata": {},
          "execution_count": 14
        }
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    {
      "cell_type": "code",
      "source": [
        "#Changing the data type from object to int\n",
        "df1['Year'] = df1['Year'].astype(int)\n",
        "df1['Duration'] = df1['Duration'].astype(int)\n",
        "df1['Votes'] = df1['Votes'].astype(int)\n",
        "df1.info()\n"
      ],
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        "colab": {
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        },
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        "collapsed": true
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5659 entries, 1 to 15508\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   Name      5659 non-null   object \n",
            " 1   Year      5659 non-null   int64  \n",
            " 2   Duration  5659 non-null   int64  \n",
            " 3   Genre     5659 non-null   object \n",
            " 4   Rating    5659 non-null   float64\n",
            " 5   Votes     5659 non-null   int64  \n",
            " 6   Director  5659 non-null   object \n",
            " 7   Actor 1   5659 non-null   object \n",
            " 8   Actor 2   5659 non-null   object \n",
            " 9   Actor 3   5659 non-null   object \n",
            "dtypes: float64(1), int64(3), object(6)\n",
            "memory usage: 615.4+ KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "# Splitting the 'Genre' column by comma and space\n",
        "df1['Genre'] = df1['Genre'].str.split(', ')\n",
        "\n",
        "# Exploding the list so that each genre gets its own row\n",
        "df1 = df1.explode('Genre')\n",
        "\n",
        "# Filling any null values in the 'Genre' column with the most frequent (mode) genre\n",
        "df1['Genre'].fillna(df1['Genre'].mode()[0], inplace=True)\n",
        "\n",
        "# Removing duplicates to ensure each combination of Genre and other columns is unique\n",
        "df1.drop_duplicates(subset=['Name', 'Year', 'Genre'], inplace=True)"
      ],
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        "id": "Natkd9tg0Ivl",
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        "df1.head(10)"
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              "                              Name  Year  Duration    Genre  Rating  Votes  \\\n",
              "1  Gadhvi He thought he was Gandhi  2019       109    Drama     7.0      8   \n",
              "3                           Yaaram  2019       110   Comedy     4.4     35   \n",
              "3                           Yaaram  2019       110  Romance     4.4     35   \n",
              "5                Aur Pyaar Ho Gaya  1997       147   Comedy     4.7    827   \n",
              "5                Aur Pyaar Ho Gaya  1997       147    Drama     4.7    827   \n",
              "5                Aur Pyaar Ho Gaya  1997       147  Musical     4.7    827   \n",
              "6                           Yahaan  2005       142    Drama     7.4   1086   \n",
              "6                           Yahaan  2005       142  Romance     7.4   1086   \n",
              "6                           Yahaan  2005       142      War     7.4   1086   \n",
              "8                  A Question Mark  2012        82   Horror     5.6    326   \n",
              "\n",
              "         Director          Actor 1                 Actor 2          Actor 3  \n",
              "1   Gaurav Bakshi     Rasika Dugal          Vivek Ghamande    Arvind Jangid  \n",
              "3      Ovais Khan          Prateik              Ishita Raj  Siddhant Kapoor  \n",
              "3      Ovais Khan          Prateik              Ishita Raj  Siddhant Kapoor  \n",
              "5    Rahul Rawail       Bobby Deol  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "5    Rahul Rawail       Bobby Deol  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "5    Rahul Rawail       Bobby Deol  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "6  Shoojit Sircar  Jimmy Sheirgill          Minissha Lamba   Yashpal Sharma  \n",
              "6  Shoojit Sircar  Jimmy Sheirgill          Minissha Lamba   Yashpal Sharma  \n",
              "6  Shoojit Sircar  Jimmy Sheirgill          Minissha Lamba   Yashpal Sharma  \n",
              "8   Allyson Patel        Yash Dave          Muntazir Ahmad     Kiran Bhatia  "
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              "      <td>4.7</td>\n",
              "      <td>827</td>\n",
              "      <td>Rahul Rawail</td>\n",
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              "      <td>Shammi Kapoor</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Aur Pyaar Ho Gaya</td>\n",
              "      <td>1997</td>\n",
              "      <td>147</td>\n",
              "      <td>Drama</td>\n",
              "      <td>4.7</td>\n",
              "      <td>827</td>\n",
              "      <td>Rahul Rawail</td>\n",
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              "      <td>Shammi Kapoor</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Aur Pyaar Ho Gaya</td>\n",
              "      <td>1997</td>\n",
              "      <td>147</td>\n",
              "      <td>Musical</td>\n",
              "      <td>4.7</td>\n",
              "      <td>827</td>\n",
              "      <td>Rahul Rawail</td>\n",
              "      <td>Bobby Deol</td>\n",
              "      <td>Aishwarya Rai Bachchan</td>\n",
              "      <td>Shammi Kapoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Yahaan</td>\n",
              "      <td>2005</td>\n",
              "      <td>142</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.4</td>\n",
              "      <td>1086</td>\n",
              "      <td>Shoojit Sircar</td>\n",
              "      <td>Jimmy Sheirgill</td>\n",
              "      <td>Minissha Lamba</td>\n",
              "      <td>Yashpal Sharma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Yahaan</td>\n",
              "      <td>2005</td>\n",
              "      <td>142</td>\n",
              "      <td>Romance</td>\n",
              "      <td>7.4</td>\n",
              "      <td>1086</td>\n",
              "      <td>Shoojit Sircar</td>\n",
              "      <td>Jimmy Sheirgill</td>\n",
              "      <td>Minissha Lamba</td>\n",
              "      <td>Yashpal Sharma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Yahaan</td>\n",
              "      <td>2005</td>\n",
              "      <td>142</td>\n",
              "      <td>War</td>\n",
              "      <td>7.4</td>\n",
              "      <td>1086</td>\n",
              "      <td>Shoojit Sircar</td>\n",
              "      <td>Jimmy Sheirgill</td>\n",
              "      <td>Minissha Lamba</td>\n",
              "      <td>Yashpal Sharma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>A Question Mark</td>\n",
              "      <td>2012</td>\n",
              "      <td>82</td>\n",
              "      <td>Horror</td>\n",
              "      <td>5.6</td>\n",
              "      <td>326</td>\n",
              "      <td>Allyson Patel</td>\n",
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              "      <td>Muntazir Ahmad</td>\n",
              "      <td>Kiran Bhatia</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 11978,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5363,\n        \"samples\": [\n          \"Uri The Surgical Strike\",\n          \"Ek Krantiveer Vasudev Balwant Phadke\",\n          \"Qayamat City Under Threat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 1931,\n        \"max\": 2021,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          1990,\n          1974,\n          1970\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 21,\n        \"max\": 321,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          197,\n          247,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Drama\",\n          \"Sport\",\n          \"Action\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.374268925936443,\n        \"min\": 1.1,\n        \"max\": 10.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          8.4,\n          7.0,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15306,\n        \"min\": 5,\n        \"max\": 591417,\n        \"num_unique_values\": 2027,\n        \"samples\": [\n          2534,\n          8372,\n          21773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2431,\n        \"samples\": [\n          \"Paresh Vinodray Savani\",\n          \"Ramesh Saigal\",\n          \"Omung Kumar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1960,\n        \"samples\": [\n          \"Mohan Kapur\",\n          \"Samir Aftab\",\n          \"Riya Deepsi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2321,\n        \"samples\": [\n          \"Arjun Fauzdar\",\n          \"Tara Deshpande\",\n          \"Saurabh Shukla\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2556,\n        \"samples\": [\n          \"Reema Sen\",\n          \"Nitya Shetty\",\n          \"Siddharth\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#New Exploratory_Data_Analysis on cleaned data"
      ],
      "metadata": {
        "id": "nex-SUW02iu5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Resetting the Index"
      ],
      "metadata": {
        "id": "sHoc6k-P3bCn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1.reset_index(drop=True, inplace=True)\n",
        "df1.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "r-HiJaCs3Gc6",
        "outputId": "eac7eb19-fb03-4263-abcd-9ee1a5557774",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              Name  Year  Duration    Genre  Rating  Votes  \\\n",
              "0  Gadhvi He thought he was Gandhi  2019       109    Drama     7.0      8   \n",
              "1                           Yaaram  2019       110   Comedy     4.4     35   \n",
              "2                           Yaaram  2019       110  Romance     4.4     35   \n",
              "3                Aur Pyaar Ho Gaya  1997       147   Comedy     4.7    827   \n",
              "4                Aur Pyaar Ho Gaya  1997       147    Drama     4.7    827   \n",
              "5                Aur Pyaar Ho Gaya  1997       147  Musical     4.7    827   \n",
              "6                           Yahaan  2005       142    Drama     7.4   1086   \n",
              "7                           Yahaan  2005       142  Romance     7.4   1086   \n",
              "8                           Yahaan  2005       142      War     7.4   1086   \n",
              "9                  A Question Mark  2012        82   Horror     5.6    326   \n",
              "\n",
              "         Director          Actor 1                 Actor 2          Actor 3  \n",
              "0   Gaurav Bakshi     Rasika Dugal          Vivek Ghamande    Arvind Jangid  \n",
              "1      Ovais Khan          Prateik              Ishita Raj  Siddhant Kapoor  \n",
              "2      Ovais Khan          Prateik              Ishita Raj  Siddhant Kapoor  \n",
              "3    Rahul Rawail       Bobby Deol  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "4    Rahul Rawail       Bobby Deol  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "5    Rahul Rawail       Bobby Deol  Aishwarya Rai Bachchan    Shammi Kapoor  \n",
              "6  Shoojit Sircar  Jimmy Sheirgill          Minissha Lamba   Yashpal Sharma  \n",
              "7  Shoojit Sircar  Jimmy Sheirgill          Minissha Lamba   Yashpal Sharma  \n",
              "8  Shoojit Sircar  Jimmy Sheirgill          Minissha Lamba   Yashpal Sharma  \n",
              "9   Allyson Patel        Yash Dave          Muntazir Ahmad     Kiran Bhatia  "
            ],
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              "      <td>Aur Pyaar Ho Gaya</td>\n",
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              "      <td>4.7</td>\n",
              "      <td>827</td>\n",
              "      <td>Rahul Rawail</td>\n",
              "      <td>Bobby Deol</td>\n",
              "      <td>Aishwarya Rai Bachchan</td>\n",
              "      <td>Shammi Kapoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Aur Pyaar Ho Gaya</td>\n",
              "      <td>1997</td>\n",
              "      <td>147</td>\n",
              "      <td>Drama</td>\n",
              "      <td>4.7</td>\n",
              "      <td>827</td>\n",
              "      <td>Rahul Rawail</td>\n",
              "      <td>Bobby Deol</td>\n",
              "      <td>Aishwarya Rai Bachchan</td>\n",
              "      <td>Shammi Kapoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Aur Pyaar Ho Gaya</td>\n",
              "      <td>1997</td>\n",
              "      <td>147</td>\n",
              "      <td>Musical</td>\n",
              "      <td>4.7</td>\n",
              "      <td>827</td>\n",
              "      <td>Rahul Rawail</td>\n",
              "      <td>Bobby Deol</td>\n",
              "      <td>Aishwarya Rai Bachchan</td>\n",
              "      <td>Shammi Kapoor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Yahaan</td>\n",
              "      <td>2005</td>\n",
              "      <td>142</td>\n",
              "      <td>Drama</td>\n",
              "      <td>7.4</td>\n",
              "      <td>1086</td>\n",
              "      <td>Shoojit Sircar</td>\n",
              "      <td>Jimmy Sheirgill</td>\n",
              "      <td>Minissha Lamba</td>\n",
              "      <td>Yashpal Sharma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Yahaan</td>\n",
              "      <td>2005</td>\n",
              "      <td>142</td>\n",
              "      <td>Romance</td>\n",
              "      <td>7.4</td>\n",
              "      <td>1086</td>\n",
              "      <td>Shoojit Sircar</td>\n",
              "      <td>Jimmy Sheirgill</td>\n",
              "      <td>Minissha Lamba</td>\n",
              "      <td>Yashpal Sharma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Yahaan</td>\n",
              "      <td>2005</td>\n",
              "      <td>142</td>\n",
              "      <td>War</td>\n",
              "      <td>7.4</td>\n",
              "      <td>1086</td>\n",
              "      <td>Shoojit Sircar</td>\n",
              "      <td>Jimmy Sheirgill</td>\n",
              "      <td>Minissha Lamba</td>\n",
              "      <td>Yashpal Sharma</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>A Question Mark</td>\n",
              "      <td>2012</td>\n",
              "      <td>82</td>\n",
              "      <td>Horror</td>\n",
              "      <td>5.6</td>\n",
              "      <td>326</td>\n",
              "      <td>Allyson Patel</td>\n",
              "      <td>Yash Dave</td>\n",
              "      <td>Muntazir Ahmad</td>\n",
              "      <td>Kiran Bhatia</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 11978,\n  \"fields\": [\n    {\n      \"column\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5363,\n        \"samples\": [\n          \"Uri The Surgical Strike\",\n          \"Ek Krantiveer Vasudev Balwant Phadke\",\n          \"Qayamat City Under Threat\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 1931,\n        \"max\": 2021,\n        \"num_unique_values\": 91,\n        \"samples\": [\n          1990,\n          1974,\n          1970\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 21,\n        \"max\": 321,\n        \"num_unique_values\": 174,\n        \"samples\": [\n          197,\n          247,\n          86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 22,\n        \"samples\": [\n          \"Drama\",\n          \"Sport\",\n          \"Action\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.374268925936443,\n        \"min\": 1.1,\n        \"max\": 10.0,\n        \"num_unique_values\": 83,\n        \"samples\": [\n          8.4,\n          7.0,\n          5.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15306,\n        \"min\": 5,\n        \"max\": 591417,\n        \"num_unique_values\": 2027,\n        \"samples\": [\n          2534,\n          8372,\n          21773\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2431,\n        \"samples\": [\n          \"Paresh Vinodray Savani\",\n          \"Ramesh Saigal\",\n          \"Omung Kumar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 1\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1960,\n        \"samples\": [\n          \"Mohan Kapur\",\n          \"Samir Aftab\",\n          \"Riya Deepsi\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 2\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2321,\n        \"samples\": [\n          \"Arjun Fauzdar\",\n          \"Tara Deshpande\",\n          \"Saurabh Shukla\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Actor 3\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2556,\n        \"samples\": [\n          \"Reema Sen\",\n          \"Nitya Shetty\",\n          \"Siddharth\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove duplicates based on the 'Name' column and keep the first occurrence\n",
        "df1 = df1.drop_duplicates(subset='Name', keep='first')"
      ],
      "metadata": {
        "id": "R7WrerKtQ-e9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling the outliers with z-score"
      ],
      "metadata": {
        "id": "ZFcpF_H86jMz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting the EDA to see the basic calculation\n",
        "df1.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "collapsed": true,
        "id": "Uo_Y72A47fTb",
        "outputId": "7c5fa248-d1ec-4961-a80f-43dd050a9347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Year     Duration       Rating          Votes\n",
              "count  5363.000000  5363.000000  5363.000000    5363.000000\n",
              "mean   1996.419728   133.042886     5.912717    2680.202872\n",
              "std      19.712718    25.470530     1.379154   13896.686378\n",
              "min    1931.000000    21.000000     1.100000       5.000000\n",
              "25%    1983.000000   118.000000     5.000000      30.000000\n",
              "50%    2002.000000   135.000000     6.100000     128.000000\n",
              "75%    2013.000000   150.000000     6.900000     903.500000\n",
              "max    2021.000000   321.000000    10.000000  591417.000000"
            ],
            "text/html": [
              "\n",
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              "      <th>Year</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5363.000000</td>\n",
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              "      <td>5363.000000</td>\n",
              "      <td>5363.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1996.419728</td>\n",
              "      <td>133.042886</td>\n",
              "      <td>5.912717</td>\n",
              "      <td>2680.202872</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
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              "      <td>25.470530</td>\n",
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              "      <td>13896.686378</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>1.100000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1983.000000</td>\n",
              "      <td>118.000000</td>\n",
              "      <td>5.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>6.100000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2013.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>903.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2021.000000</td>\n",
              "      <td>321.000000</td>\n",
              "      <td>10.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1464.6383955105948,\n        \"min\": 19.712717551662948,\n        \"max\": 5363.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1996.4197277643111,\n          2002.0,\n          5363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1852.7817062943368,\n        \"min\": 21.0,\n        \"max\": 5363.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          133.0428864441544,\n          135.0,\n          5363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Rating\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1894.270980044816,\n        \"min\": 1.1,\n        \"max\": 5363.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.91271676300578,\n          6.1,\n          5363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Votes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207988.47907719095,\n        \"min\": 5.0,\n        \"max\": 591417.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2680.2028715271304,\n          128.0,\n          5363.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Import stats and perform Z-score\n",
        "import scipy.stats as stats\n",
        "df1 = df1[(np.abs(stats.zscore(df1[['Rating', 'Votes', 'Duration']], nan_policy='omit')) < 3).all(axis=1)]"
      ],
      "metadata": {
        "id": "DZ-PFIlC6kJO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "MsUhNd8_8aOM",
        "outputId": "d1f00b5a-dd71-47af-e987-5daf7595cdcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5260, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#\"Unveiling Insights: A Dive into Univariate Analysis\""
      ],
      "metadata": {
        "id": "7UhwWcXJBbek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the histogram plot\n",
        "import plotly.express as px\n",
        "fig = px.histogram(data_frame=df1, x=\"Duration\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "3JyLytZoB7IH",
        "outputId": "e8b545a5-a3e7-40ae-deee-af1df1601ef2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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    },
    {
      "cell_type": "code",
      "source": [
        "#Performing the histogram plot\n",
        "fig = px.histogram(data_frame=df1, x=\"Rating\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "DciSMgQPAnEE",
        "outputId": "075834de-71d3-4162-db12-80f5074a7084"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "'Duration' and 'Rating' Show a Symmetrical, Bell-Curve (Normal) Distribution\""
      ],
      "metadata": {
        "id": "_B0gFSmLDFJ-"
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      "cell_type": "code",
      "source": [
        "#Performing the violin plot\n",
        "fig = px.violin(data_frame=df1, x=\"Votes\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "w-7ul29uCFkj",
        "outputId": "6696f5b0-7994-4bc3-d97d-7c3df32820e7"
      },
      "execution_count": null,
      "outputs": [
        {
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      "source": [
        "'Votes' demonstrates a rightward skew in its distribution"
      ],
      "metadata": {
        "id": "59tBxdWZDR1d"
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    },
    {
      "cell_type": "code",
      "source": [
        "#Top 3 categories in Genre with no of movies\n",
        "g_cnt = df1['Genre'].value_counts().reset_index()\n",
        "g_cnt.columns = ['Genre', 'Count']\n",
        "g_cnt"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "18T4rSWJFYJO",
        "outputId": "42f1ac31-5ca7-4648-e960-ef0e1fd53b51",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Genre  Count\n",
              "0         Drama   1717\n",
              "1        Action   1502\n",
              "2        Comedy    942\n",
              "3         Crime    251\n",
              "4       Romance    151\n",
              "5        Horror    119\n",
              "6     Adventure     97\n",
              "7      Thriller     86\n",
              "8       Musical     82\n",
              "9     Biography     76\n",
              "10      Mystery     54\n",
              "11       Family     50\n",
              "12  Documentary     45\n",
              "13    Animation     39\n",
              "14      Fantasy     29\n",
              "15      History      8\n",
              "16       Sci-Fi      4\n",
              "17          War      3\n",
              "18        Music      3\n",
              "19        Sport      2"
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              "    <style>\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
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              "      }\n",
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              "\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('g_cnt')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('g_cnt');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "g_cnt",
              "summary": "{\n  \"name\": \"g_cnt\",\n  \"rows\": 20,\n  \"fields\": [\n    {\n      \"column\": \"Genre\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 20,\n        \"samples\": [\n          \"Drama\",\n          \"War\",\n          \"History\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 505,\n        \"min\": 2,\n        \"max\": 1717,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1717,\n          119,\n          50\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Analysis of Year with Best rating"
      ],
      "metadata": {
        "id": "rHc_Lmfu5Qmg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Group by Year and calculate avg-Rating\n",
        "avg_ratings = df1.groupby('Year')['Rating'].mean().reset_index()\n",
        "\n",
        "Best_year = avg_ratings.loc[avg_ratings['Rating'].idxmax()]\n",
        "\n",
        "print(f\"The year with the best average rating is {int(Best_year['Year'])} with an average rating of {Best_year['Rating']:.2f}.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q19JEt35GhTg",
        "outputId": "0a675d23-9d56-4e79-9197-a430f292d403"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The year with the best average rating is 1952 with an average rating of 7.21.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Same output on Line-plot for better Visualization\n",
        "fig = px.line(data_frame=avg_ratings, x='Year', y='Rating',\n",
        "              title='Average Movie Ratings by Year',\n",
        "              labels={'Year': 'Year', 'Rating': 'Average Rating'},\n",
        "              markers=True)  # Adds markers for better visibility\n",
        "\n",
        "fig.update_layout(xaxis_title='Year', yaxis_title='Average Rating')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "KYsvL9bHJKXO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "28afc77d-0589-4456-b437-7f92ffcc3325"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* Does length of movie have any impact with the rating?"
      ],
      "metadata": {
        "id": "oOxqggWDL7WE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Descriptive statistics Analysis\n",
        "des_stats = df1[['Duration', 'Rating']].describe()\n",
        "print(des_stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzwqXLcaMHbL",
        "outputId": "93437f20-9b8c-4316-ddf6-81adca3d4195",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          Duration       Rating\n",
            "count  5260.000000  5260.000000\n",
            "mean    132.770722     5.898992\n",
            "std      24.006217     1.359466\n",
            "min      57.000000     1.800000\n",
            "25%     118.000000     5.000000\n",
            "50%     135.000000     6.100000\n",
            "75%     150.000000     6.900000\n",
            "max     208.000000    10.000000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate correlation coefficient\n",
        "correlation = df1['Duration'].corr(df1['Rating'])\n",
        "print(f'Correlation between Duration and Rating: {correlation:.2f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoP9md9iM6Ju",
        "outputId": "27764387-9819-414e-ace4-4c975321ce7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Correlation between Duration and Rating: -0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Scatter plot\n",
        "fig = px.scatter(df1, x='Duration', y='Rating',\n",
        "                 title='Movie Duration vs Rating',\n",
        "                 labels={'Duration': 'Movie Duration (minutes)', 'Rating': 'Movie Rating'},\n",
        "                 trendline='ols') #Adding a trendline\n",
        "fig.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "3eA670GjOG_i",
        "outputId": "7e21783d-a196-484d-a8dd-62d49ccc6ddf"
      },
      "execution_count": null,
      "outputs": [
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Based on above 3 different analysis:\n",
        "\n",
        "* There is a very weak correlation between the length of a movie and its rating, as indicated by the low correlation coefficient and low R-squared value in the regression analysis.\n",
        "\n",
        "* While the relationship is statistically significant, the practical significance is negligible, meaning that other factors are likely far more important in determining movie ratings."
      ],
      "metadata": {
        "id": "lH_p9ilbOmJF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#10 movies according to rating per year and overall."
      ],
      "metadata": {
        "id": "dfFLG9qFPGdr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort the dataframe by rating in descending order\n",
        "top_10_overall = df1.sort_values(by='Rating', ascending=False).head(10)\n",
        "\n",
        "# output\n",
        "print(\"Top 10 Movies by Rating (Overall):\")\n",
        "print(top_10_overall[['Name', 'Rating', 'Year']])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jfzAxirFPO28",
        "outputId": "ce8254c0-a629-490c-ad71-85abe16af4fc",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 10 Movies by Rating (Overall):\n",
            "                      Name  Rating  Year\n",
            "6712       Love Qubool Hai    10.0  2020\n",
            "4306            Half Songs     9.7  2021\n",
            "10943  The Reluctant Crime     9.4  2020\n",
            "4031          Gho Gho Rani     9.4  2019\n",
            "5488                  June     9.4  2021\n",
            "4074           God of gods     9.3  2019\n",
            "1141          Ashok Vatika     9.3  2018\n",
            "10129           Sindhustan     9.3  2019\n",
            "9173                Reflct     9.3  2021\n",
            "6717          Love Sorries     9.3  2021\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Number of popular movies released each year"
      ],
      "metadata": {
        "id": "P73-pPH5YS3r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Popularity based on more than rating 7.0 and votes 1000\n",
        "pop_movies = df1[(df1['Rating'] > 7.0) & (df1['Votes'] > 1000)]\n",
        "\n",
        "per_year = pop_movies['Year'].value_counts().sort_index()\n",
        "\n",
        "print(per_year)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "1VksDAWWhAim",
        "outputId": "a2ca90e3-8f66-4213-b787-dbd8fc7bc384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Year\n",
            "1951     1\n",
            "1953     1\n",
            "1955     1\n",
            "1957     4\n",
            "1958     2\n",
            "        ..\n",
            "2017    16\n",
            "2018    24\n",
            "2019    26\n",
            "2020    17\n",
            "2021     4\n",
            "Name: count, Length: 64, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Counting the number of votes which movies preformed better in rating per year and overall"
      ],
      "metadata": {
        "id": "EXTKHYSdj16a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Per year\n",
        "per_year = df1.loc[df1.groupby('Year')['Rating'].idxmax()]\n",
        "\n",
        "#Sort by Year\n",
        "per_year = per_year.sort_values('Year')\n",
        "\n",
        "#Overall rating\n",
        "overall = df1.loc[df1['Rating'].idxmax()]\n",
        "\n",
        "print(\"Best Movie per Year (with Number of Votes):\")\n",
        "print(per_year[['Year', 'Name', 'Rating', 'Votes']])\n",
        "\n",
        "print(\"\\nBest Movie Overall (with Number of Votes):\")\n",
        "print(overall[['Name', 'Year', 'Rating', 'Votes']])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "oC-XZ9yOj-B6",
        "outputId": "f36f3e1b-ea51-46ab-d066-ff4ca9cc77e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Movie per Year (with Number of Votes):\n",
            "       Year                    Name  Rating  Votes\n",
            "10900  1931  The Light of the World     6.2    112\n",
            "3694   1933                    Fate     6.2     12\n",
            "7170   1934                 Mazdoor     8.5      6\n",
            "4899   1935                Inquilab     7.4     38\n",
            "5316   1936             Jeevan Naya     7.3      6\n",
            "...     ...                     ...     ...    ...\n",
            "9171   2017     Rediscovering India     9.0     62\n",
            "1141   2018            Ashok Vatika     9.3      7\n",
            "4031   2019            Gho Gho Rani     9.4     47\n",
            "6712   2020         Love Qubool Hai    10.0      5\n",
            "4306   2021              Half Songs     9.7      7\n",
            "\n",
            "[90 rows x 4 columns]\n",
            "\n",
            "Best Movie Overall (with Number of Votes):\n",
            "Name      Love Qubool Hai\n",
            "Year                 2020\n",
            "Rating               10.0\n",
            "Votes                   5\n",
            "Name: 6712, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Which director directed the most movies"
      ],
      "metadata": {
        "id": "DKwO014ilyIJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Count the number of movies directed by each director\n",
        "d_m_count = df1['Director'].value_counts()\n",
        "\n",
        "#Director and the movies he directed\n",
        "director_name = d_m_count.idxmax()\n",
        "max_count = d_m_count.max()\n",
        "\n",
        "print(f\"The director who directed the most movies is {director_name}, with {max_count} movies.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtXP9duXl1Tj",
        "outputId": "1f1ba08e-5f3c-4dee-ee08-dfd6848d1d6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The director who directed the most movies is David Dhawan, with 37 movies.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Which actor starred in the movie"
      ],
      "metadata": {
        "id": "nJlNX5XfnxeF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if 'Actor 1' in df1.columns:\n",
        "    star_actors = df1[['Name', 'Actor 1']]\n",
        "\n",
        "#Rename 'Actor 1' column to 'Star Actor'\n",
        "    star_actors = star_actors.rename(columns={'Actor 1': 'Star Actor'})\n",
        "\n",
        "    print(star_actors)\n",
        "else:\n",
        "    print(\"The 'Actor 1' column does not exist in the dataset.\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxQWymi0nzPP",
        "outputId": "4f7e8bc6-5a42-4f12-8de8-56713a7cbeab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  Name       Star Actor\n",
            "0      Gadhvi He thought he was Gandhi     Rasika Dugal\n",
            "1                               Yaaram          Prateik\n",
            "3                    Aur Pyaar Ho Gaya       Bobby Deol\n",
            "6                               Yahaan  Jimmy Sheirgill\n",
            "9                      A Question Mark        Yash Dave\n",
            "...                                ...              ...\n",
            "11967                           Zubaan    Vicky Kaushal\n",
            "11968                         Zubeidaa   Karisma Kapoor\n",
            "11971                  Zulm Ki Zanjeer      Chiranjeevi\n",
            "11974                            Zulmi     Akshay Kumar\n",
            "11976                     Zulm-O-Sitam       Dharmendra\n",
            "\n",
            "[5260 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Data Preprocessing"
      ],
      "metadata": {
        "id": "tErEQ8YNHjo2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1.copy()"
      ],
      "metadata": {
        "id": "aYJlnp5EJESD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Genre encoding\n",
        "genre_mean_rating = df2.groupby('Genre')['Rating'].transform('mean')\n",
        "df2['Genre_mean_rating'] = genre_mean_rating\n",
        "\n",
        "# Director encoding\n",
        "director_mean_rating = df2.groupby('Director')['Rating'].transform('mean')\n",
        "df2['Director_encoded'] = director_mean_rating\n",
        "\n",
        "#Actor 1 encoding\n",
        "actor1_mean_rating = df2.groupby('Actor 1')['Rating'].transform('mean')\n",
        "df2['Actor1_encoded'] = actor1_mean_rating\n",
        "\n",
        "#Actor 2 encoding\n",
        "actor2_mean_rating = df2.groupby('Actor 2')['Rating'].transform('mean')\n",
        "df2['Actor2_encoded'] = actor2_mean_rating\n",
        "\n",
        "#Actor 3 encoding\n",
        "actor3_mean_rating = df2.groupby('Actor 3')['Rating'].transform('mean')\n",
        "df2['Actor3_encoded'] = actor3_mean_rating"
      ],
      "metadata": {
        "id": "Kku5hAEyHqmS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = df2[['Year', 'Duration', 'Votes','Genre_mean_rating','Director_encoded','Actor1_encoded', 'Actor2_encoded', 'Actor3_encoded']]\n",
        "y = df2['Rating']\n",
        "\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
      ],
      "metadata": {
        "id": "EMTADjUtGpKI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model Building and Evaluation"
      ],
      "metadata": {
        "id": "QLvuAq0CKwUw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "rf = RandomForestRegressor()\n",
        "rf.fit(X_train,y_train)\n",
        "rf_pred = rf.predict(X_test)"
      ],
      "metadata": {
        "id": "c3F89uh2KzLd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
        "print('MSE: ',mean_squared_error(y_test, rf_pred))\n",
        "print('MAE: ',mean_absolute_error(y_test, rf_pred))\n",
        "print('R2: ',r2_score(y_test, rf_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Xgn_NyQNCu3",
        "outputId": "80bb219f-82b7-40e4-ccba-87d8d041bab3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MSE:  0.309585830798479\n",
            "MAE:  0.36932129277566544\n",
            "R2:  0.8369568449673469\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By. Girish Kumar (Intership_Trainee@CodSoft)"
      ],
      "metadata": {
        "id": "udZPyO2G0DJ6"
      }
    }
  ]
}