Visualization and Binary Classification_Kickstarter.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5-ajev9pMAs3"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "sns.set(font_scale = 1)\n",
        "%matplotlib inline\n",
        "plt.style.use('ggplot')\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from IPython.core.pylabtools import figsize\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PKYjX0fwMlVa"
      },
      "outputs": [],
      "source": [
        "#Reading the data \n",
        "kickstarter_df = pd.read_csv('/content/kickstarter_data_full.csv', index_col=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xcKbCAUxNBmr",
        "outputId": "f0b31781-631d-41d6-d035-1fbaf8f7a929"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(20632, 67)"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Checking the shape of dataset\n",
        "kickstarter_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qj2AHjX5Le2F",
        "outputId": "e9386516-a7cc-4b48-f4ee-4407de8fc3e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Percentage of missing values in the dataset: 6.09%\n"
          ]
        }
      ],
      "source": [
        "total_cells = np.product(kickstarter_df.shape)\n",
        "total_missing = kickstarter_df.isnull().sum().sum()\n",
        "percent_missing = (total_missing/total_cells) * 100\n",
        "print(\"Percentage of missing values in the dataset: {:.2f}%\".format(percent_missing))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x3arNF09NP5q",
        "outputId": "d87c3cd3-6c51-4e78-fc47-5034c5a586f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 20632 entries, 0 to 20631\n",
            "Data columns (total 67 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   id                           20632 non-null  int64  \n",
            " 1   photo                        20632 non-null  object \n",
            " 2   name                         20632 non-null  object \n",
            " 3   blurb                        20627 non-null  object \n",
            " 4   goal                         20632 non-null  float64\n",
            " 5   pledged                      20632 non-null  float64\n",
            " 6   state                        20632 non-null  object \n",
            " 7   slug                         20632 non-null  object \n",
            " 8   disable_communication        20632 non-null  bool   \n",
            " 9   country                      20632 non-null  object \n",
            " 10  currency                     20632 non-null  object \n",
            " 11  currency_symbol              20632 non-null  object \n",
            " 12  currency_trailing_code       20632 non-null  bool   \n",
            " 13  deadline                     20632 non-null  object \n",
            " 14  state_changed_at             20632 non-null  object \n",
            " 15  created_at                   20632 non-null  object \n",
            " 16  launched_at                  20632 non-null  object \n",
            " 17  staff_pick                   20632 non-null  bool   \n",
            " 18  backers_count                20632 non-null  int64  \n",
            " 19  static_usd_rate              20632 non-null  float64\n",
            " 20  usd_pledged                  20632 non-null  float64\n",
            " 21  creator                      20632 non-null  object \n",
            " 22  location                     20587 non-null  object \n",
            " 23  category                     18743 non-null  object \n",
            " 24  profile                      20632 non-null  object \n",
            " 25  spotlight                    20632 non-null  bool   \n",
            " 26  urls                         20632 non-null  object \n",
            " 27  source_url                   20632 non-null  object \n",
            " 28  friends                      60 non-null     object \n",
            " 29  is_starred                   60 non-null     object \n",
            " 30  is_backing                   60 non-null     object \n",
            " 31  permissions                  60 non-null     object \n",
            " 32  name_len                     20627 non-null  float64\n",
            " 33  name_len_clean               20627 non-null  float64\n",
            " 34  blurb_len                    20627 non-null  float64\n",
            " 35  blurb_len_clean              20627 non-null  float64\n",
            " 36  deadline_weekday             20632 non-null  object \n",
            " 37  state_changed_at_weekday     20632 non-null  object \n",
            " 38  created_at_weekday           20632 non-null  object \n",
            " 39  launched_at_weekday          20632 non-null  object \n",
            " 40  deadline_month               20632 non-null  int64  \n",
            " 41  deadline_day                 20632 non-null  int64  \n",
            " 42  deadline_yr                  20632 non-null  int64  \n",
            " 43  deadline_hr                  20632 non-null  int64  \n",
            " 44  state_changed_at_month       20632 non-null  int64  \n",
            " 45  state_changed_at_day         20632 non-null  int64  \n",
            " 46  state_changed_at_yr          20632 non-null  int64  \n",
            " 47  state_changed_at_hr          20632 non-null  int64  \n",
            " 48  created_at_month             20632 non-null  int64  \n",
            " 49  created_at_day               20632 non-null  int64  \n",
            " 50  created_at_yr                20632 non-null  int64  \n",
            " 51  created_at_hr                20632 non-null  int64  \n",
            " 52  launched_at_month            20632 non-null  int64  \n",
            " 53  launched_at_day              20632 non-null  int64  \n",
            " 54  launched_at_yr               20632 non-null  int64  \n",
            " 55  launched_at_hr               20632 non-null  int64  \n",
            " 56  create_to_launch             20632 non-null  object \n",
            " 57  launch_to_deadline           20632 non-null  object \n",
            " 58  launch_to_state_change       20632 non-null  object \n",
            " 59  create_to_launch_days        20632 non-null  int64  \n",
            " 60  launch_to_deadline_days      20632 non-null  int64  \n",
            " 61  launch_to_state_change_days  20632 non-null  int64  \n",
            " 62  SuccessfulBool               20632 non-null  int64  \n",
            " 63  USorGB                       20632 non-null  int64  \n",
            " 64  TOPCOUNTRY                   20632 non-null  int64  \n",
            " 65  LaunchedTuesday              20632 non-null  int64  \n",
            " 66  DeadlineWeekend              20632 non-null  int64  \n",
            "dtypes: bool(4), float64(8), int64(26), object(29)\n",
            "memory usage: 10.2+ MB\n"
          ]
        }
      ],
      "source": [
        "#Checking number of null values for each variable\n",
        "kickstarter_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6etbsuONf9E"
      },
      "source": [
        "**We can observe that mainly four variables \"friends\", \"is_starred\",  \"is_backing\", \"permissions\" are having more than 90% of null values. In the next step we will drop these four variables.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nDg-H_nZORkh"
      },
      "outputs": [],
      "source": [
        "#Dropping the columns having more than 90% of null values.\n",
        "cols_to_drop = ['friends', 'is_starred', 'is_backing', 'permissions']\n",
        "kickstarter_df.drop(labels=cols_to_drop, axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GI_0U3kTOr3b",
        "outputId": "b6643940-c5e2-48a9-9bef-b9a283fa286a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(20632, 63)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Shape of data after dropping columns\n",
        "\n",
        "kickstarter_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W59Tev6XPZAj"
      },
      "outputs": [],
      "source": [
        "#Making a copy of data for data visualization before dropping the unnecessary columns. \n",
        "Data_Visualization = kickstarter_df.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zuawKFCoO4gS"
      },
      "outputs": [],
      "source": [
        "# Manually identified some features which are not useful for prediction and dropping them.\n",
        "\n",
        "Features_to_drop = ['id', 'name','blurb','photo','slug','currency_symbol','currency_trailing_code', \n",
        "                   'creator','location','urls','source_url','name_len','spotlight','blurb_len',\n",
        "                   'create_to_launch','launch_to_deadline','launch_to_state_change','deadline_day', \n",
        "                   'deadline_hr','state_changed_at_day', 'state_changed_at_hr', 'created_at_day', \n",
        "                   'created_at_hr','launched_at_day', 'launched_at_hr','name_len_clean','blurb_len_clean',\n",
        "                   'state_changed_at','deadline_weekday','state_changed_at_weekday','created_at_weekday',\n",
        "                   'launched_at_weekday','deadline','created_at','launched_at','create_to_launch_days', \n",
        "                   'launch_to_deadline_days','launch_to_state_change_days','SuccessfulBool','USorGB',\n",
        "                   'TOPCOUNTRY','LaunchedTuesday','DeadlineWeekend', 'profile'\n",
        "                    ]\n",
        "\n",
        "kickstarter_df.drop(labels=Features_to_drop, axis=1, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xXSeFMueRjgH"
      },
      "outputs": [],
      "source": [
        "# Around 2000 rows having null values, so dropping the rows with null values.\n",
        "\n",
        "kickstarter_clean = kickstarter_df.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPDGiI5oRORv",
        "outputId": "36d888e6-c4f4-4b44-df76-23b50208c26e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(18743, 19)"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Shape of data after dropping columns and rows\n",
        "kickstarter_clean.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dg_P9JERSSGm",
        "outputId": "e01c98d4-4658-41a2-d410-71ac1848eda0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 18743 entries, 0 to 20631\n",
            "Data columns (total 19 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   goal                    18743 non-null  float64\n",
            " 1   pledged                 18743 non-null  float64\n",
            " 2   state                   18743 non-null  object \n",
            " 3   disable_communication   18743 non-null  bool   \n",
            " 4   country                 18743 non-null  object \n",
            " 5   currency                18743 non-null  object \n",
            " 6   staff_pick              18743 non-null  bool   \n",
            " 7   backers_count           18743 non-null  int64  \n",
            " 8   static_usd_rate         18743 non-null  float64\n",
            " 9   usd_pledged             18743 non-null  float64\n",
            " 10  category                18743 non-null  object \n",
            " 11  deadline_month          18743 non-null  int64  \n",
            " 12  deadline_yr             18743 non-null  int64  \n",
            " 13  state_changed_at_month  18743 non-null  int64  \n",
            " 14  state_changed_at_yr     18743 non-null  int64  \n",
            " 15  created_at_month        18743 non-null  int64  \n",
            " 16  created_at_yr           18743 non-null  int64  \n",
            " 17  launched_at_month       18743 non-null  int64  \n",
            " 18  launched_at_yr          18743 non-null  int64  \n",
            "dtypes: bool(2), float64(4), int64(9), object(4)\n",
            "memory usage: 2.6+ MB\n"
          ]
        }
      ],
      "source": [
        "kickstarter_clean.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3OZk33fCSa0h"
      },
      "source": [
        "**We can observe that some columns are having their data type as bool and object. So we encode them.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j165CtlsyqjX",
        "outputId": "038e75f1-9f8b-41fe-bca2-d21949e9a55a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['failed' 'live' 'successful' 'canceled' 'suspended']\n"
          ]
        }
      ],
      "source": [
        "print(kickstarter_clean['state'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBT0nBIiqYTy",
        "outputId": "5adc6373-def4-47f4-baad-92eba8fa8658"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "['Academic' 'Places' 'Blues' 'Restaurants' 'Webseries' 'Thrillers'\n",
            " 'Shorts' 'Web' 'Apps' 'Gadgets' 'Hardware' 'Festivals' 'Plays' 'Musical'\n",
            " 'Flight' 'Spaces' 'Immersive' 'Experimental' 'Comedy' 'Wearables' 'Sound'\n",
            " 'Software' 'Robots' 'Makerspaces']\n",
            "['US' 'DE' 'GB' 'CA' 'AU' 'AT' 'CH' 'NZ' 'IT' 'MX' 'NL' 'SE' 'ES' 'DK'\n",
            " 'IE' 'NO' 'FR' 'BE' 'LU' 'SG' 'HK']\n",
            "['USD' 'EUR' 'GBP' 'CAD' 'AUD' 'CHF' 'NZD' 'MXN' 'SEK' 'DKK' 'NOK' 'SGD'\n",
            " 'HKD']\n",
            "[False  True]\n",
            "[False  True]\n"
          ]
        }
      ],
      "source": [
        "print(kickstarter_clean['category'].unique())\n",
        "print(kickstarter_clean['country'].unique())\n",
        "print(kickstarter_clean['currency'].unique())\n",
        "print(kickstarter_clean['disable_communication'].unique())\n",
        "print(kickstarter_clean['staff_pick'].unique())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3C6Bull2qLiQ"
      },
      "outputs": [],
      "source": [
        "# Perform categorical encoding\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "le = LabelEncoder()\n",
        "kickstarter_clean['category'] = le.fit_transform(kickstarter_clean['category'])\n",
        "kickstarter_clean['country'] = le.fit_transform(kickstarter_clean['country'])\n",
        "kickstarter_clean['currency'] = le.fit_transform(kickstarter_clean['currency'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zm0iHb53qOUM"
      },
      "outputs": [],
      "source": [
        "kickstarter_clean['disable_communication'] = kickstarter_clean['disable_communication'] * 1 #converts type bool to 0 for false and 1 for true\n",
        "kickstarter_clean['staff_pick'] = kickstarter_clean['staff_pick'] * 1 #converts type bool to 0 for false and 1 for true"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wk5s4Iiinuev"
      },
      "outputs": [],
      "source": [
        "#We are copying the data into two variables, one for binary classification and one for multi classification.\n",
        "\n",
        "kickstarter_Binary = kickstarter_clean.copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8HXpU1wCSZ8e"
      },
      "outputs": [],
      "source": [
        "#This is for Binary classification.\n",
        "category = {'successful': 1, 'cancelled': 0, 'failed': 0, 'live':0,'suspended':0}\n",
        "\n",
        "# add default value for any other state value\n",
        "default_value = 0\n",
        "kickstarter_Binary['state'] = kickstarter_Binary['state'].map(category).fillna(default_value)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOpUhsbpyfp4",
        "outputId": "deb966a9-31e7-47e7-acaa-bc1770c898e2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 18743 entries, 0 to 20631\n",
            "Data columns (total 19 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   goal                    18743 non-null  float64\n",
            " 1   pledged                 18743 non-null  float64\n",
            " 2   state                   18743 non-null  float64\n",
            " 3   disable_communication   18743 non-null  int64  \n",
            " 4   country                 18743 non-null  int64  \n",
            " 5   currency                18743 non-null  int64  \n",
            " 6   staff_pick              18743 non-null  int64  \n",
            " 7   backers_count           18743 non-null  int64  \n",
            " 8   static_usd_rate         18743 non-null  float64\n",
            " 9   usd_pledged             18743 non-null  float64\n",
            " 10  category                18743 non-null  int64  \n",
            " 11  deadline_month          18743 non-null  int64  \n",
            " 12  deadline_yr             18743 non-null  int64  \n",
            " 13  state_changed_at_month  18743 non-null  int64  \n",
            " 14  state_changed_at_yr     18743 non-null  int64  \n",
            " 15  created_at_month        18743 non-null  int64  \n",
            " 16  created_at_yr           18743 non-null  int64  \n",
            " 17  launched_at_month       18743 non-null  int64  \n",
            " 18  launched_at_yr          18743 non-null  int64  \n",
            "dtypes: float64(5), int64(14)\n",
            "memory usage: 2.9 MB\n"
          ]
        }
      ],
      "source": [
        "kickstarter_Binary.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nrCFpa2_Vzl1"
      },
      "outputs": [],
      "source": [
        "# Select only the continuous variables\n",
        "kickstarter_cont = kickstarter_clean.select_dtypes(include=['float64', 'int64'])\n",
        "\n",
        "# Use pairplot to create a grid of scatterplots for each pair of variables\n",
        "# sns.pairplot(kickstarter_cont)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZiYzcAnOViEs"
      },
      "source": [
        "# **DATA VISUALIZATION**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "Eyuq7PpLVlww",
        "outputId": "ce598cc9-9112-40fb-8c1f-63f626b44fee"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create a bar plot of state\n",
        "sns.countplot(x='state', data=Data_Visualization)\n",
        "\n",
        "plt.title('')\n",
        "plt.xlabel('Outcomes of Projects')\n",
        "plt.ylabel('Number of projects')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "ByUGd_0_YlHv",
        "outputId": "4793b26c-14ab-47cc-8d6a-bf0e3f72087d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create a bar plot of state\n",
        "sns.countplot(x='state', data=kickstarter_Binary)\n",
        "\n",
        "# set the title and axis labels\n",
        "plt.title('')\n",
        "plt.xlabel('Outcomes of Projects')\n",
        "plt.ylabel('Number of projects')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "rt6FZM07aiZP",
        "outputId": "c8ae6799-443d-4a18-88db-79d70f3bcd11"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# count the number of projects for each country\n",
        "country_counts = Data_Visualization['country'].value_counts()\n",
        "\n",
        "# create a bar plot of country_counts\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x=country_counts.index, y=country_counts.values, color='skyblue')\n",
        "\n",
        "plt.title('Number of Projects by Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Number of Projects')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "Bh0FXbUIe0bY",
        "outputId": "c2a56a0c-3c5f-4c2a-d031-78f16d10138d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# group the data by country and count the number of successful projects\n",
        "success_by_country = Data_Visualization[Data_Visualization['state'] == 'successful'].groupby('country').size().reset_index(name='count')\n",
        "\n",
        "# sort the data by count in desc order\n",
        "success_by_country = success_by_country.sort_values(by='count', ascending=False)\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='country', y='count', data=success_by_country)\n",
        "\n",
        "plt.title('Number of Successful Projects by Country')\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Number of Successful Projects')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "W2R5K-AUgGsE",
        "outputId": "a4ce7048-a580-4211-a66e-4d637f8621fb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create a new column for duration ranges\n",
        "Data_Visualization['duration_range'] = pd.cut(Data_Visualization['launch_to_deadline_days'], bins=[0, 10, 20, 30, 40, 50, 60], labels=['0-10', '10-20', '20-30', '30-40', '40-50', '50-60'])\n",
        "\n",
        "# calculate percentage of successful campaigns for each duration range\n",
        "duration_counts = Data_Visualization.groupby('duration_range')['state'].value_counts(normalize=True).mul(100).rename('percent').reset_index()\n",
        "\n",
        "# filter for successful campaigns only\n",
        "duration_success = duration_counts[duration_counts['state']=='successful']\n",
        "\n",
        "sns.barplot(x='duration_range', y='percent', data=duration_success)\n",
        "plt.title('Percentage of Successful Campaigns by Duration Range')\n",
        "plt.xlabel('Duration Range')\n",
        "plt.ylabel('Percentage')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lf46snml_yd9"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "def extract_city_name(json_str):\n",
        "    try:\n",
        "      data = json.loads(str(json_str))\n",
        "    except json.decoder.JSONDecodeError:\n",
        "      return \"Other\"\n",
        "    location_url = data['urls']['web']['location']\n",
        "    # city = location_url.split('/')[-1].split('-')[0].capitalize()\n",
        "    city = location_url.split('/')[-1].capitalize()\n",
        "    return city"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uLxqO1_PGKvD"
      },
      "outputs": [],
      "source": [
        "category = {'successful': 1, 'cancelled': 0, 'failed': 0, 'live':0,'suspended':0}\n",
        "\n",
        "# add default value for any other state value\n",
        "default_value = 0\n",
        "Data_Visualization['state'] = Data_Visualization['state'].map(category).fillna(default_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fo_kP2v2AF7B"
      },
      "outputs": [],
      "source": [
        "city_list = []\n",
        "for i in Data_Visualization['location']:\n",
        "  city_list.append(extract_city_name(str(i)))\n",
        "\n",
        "Data_Visualization['city'] = Data_Visualization['location'].apply(extract_city_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "JczQR-u1A5RF",
        "outputId": "930feb9d-bd67-471b-fc35-d21542f04920"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Group the data by city and outcome and calculate the counts of projects\n",
        "grouped = Data_Visualization.groupby(['city', 'state']).size().reset_index(name='count')\n",
        "\n",
        "# Pivot the data to have cities as rows and outcomes as columns\n",
        "pivoted = grouped.pivot(index='city', columns='state', values='count')\n",
        "\n",
        "# Select the top 6 cities by total project count\n",
        "top_cities = pivoted.sum(axis=1).sort_values(ascending=False).head(10).index\n",
        "subset = pivoted.loc[top_cities]\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "plt.figure(figsize=(16, 6))\n",
        "sns.barplot(data=subset, x=subset.index, y= 1, color='green', label='Success')\n",
        "sns.barplot(data=subset, x=subset.index, y= 0, color='red', label='Failure', bottom=subset[1])\n",
        "plt.title('Top 10 Cities by Project Count and Outcome')\n",
        "plt.xlabel('City')\n",
        "plt.ylabel('Number of Projects')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 666
        },
        "id": "0au6slSpGtNR",
        "outputId": "22424da2-d977-468a-f790-d415a72590e3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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OHz8OJycnzJkzh1v2romo3/XgNGnSJGzbtg0zZszAmTNnsG7dugblWVLatm0LAFi5ciX09fXF1r/d8L89uWhOTg4AoEOHDtyyms79Sy5jETk5OYwaNQq7du3CkCFDanxYaMz5t23bFtnZ2dyvNl+K3377DQDg6+sLX19f3rr8/HzeZKwNrb9fOknW3b59+6JXr16Ii4tDly5dIC8vD2tr6ybN/6ekrvfCuhA9dEZERHBfIKp71y+xn6PGlOuoUaPg7++PY8eO4cmTJ+jWrRsGDhzYZHn9EOpyX3xfmYmoqqpCVlZW7N5Z/b4qIi8v/959idLMmzcP8+bNw6NHjxAXF4fw8HBoaGjUOQgmCcOGDYO2tjbu378PoP7XUm5uLjp27Mh9zsnJgZycHK/30dtat24NKSkpzJ07FyNHjhRb/75t66u2tq5NmzYwNTXFlClTxLZ9O4DSmDZRVlYWffv2xeXLl1FSUiIWMCspKcHly5fRt29fqKioQFpaWuzlP3XRpk2bGutkTk6O2JfzmtLl5uZCW1ub25eUlBT2799f4xsdtbS0uP839fOCjY0NwsPD8cMPP3A/iony+PDhQ7H0ubm5vPPt378/OnfujISEBGhpaeHs2bPw8/Pj7Qdo+jakvLwchYWFvLzl5uZy9bG+30UaW+737t3Ds2fPYG9vX+cyaNu27Xvr5pdals0BBZxIg5WWlkJOTo53oYgesOpLdGO4f/8++vXrB+BNY33jxg3o6elx6crKyniNT2VlpdhkbrWxt7dHcHAwvLy8oKCg8NG/aH311Vfo1KkT0tPT4ezsXGv6U6dO8bpynjhxAmpqajXeXKv7ksu4ukmTJiE3NxeOjo41rm/M+RsbG+Onn37C77//Ljah8+fq1atXSE5OxsiRI7m3G4nk5ORg0aJFSExMhEAgANDw+kskW3cnTZqErVu3ol27drC1tRX7EvI5q+u9sC769u0LRUVFZGVliQ2h+dI0plzl5eVhZ2eH2NhY5OTkYPr06c36Ibyu98XevXsDAB48eMAFT+7fv8/7BVxGRga9e/dGcnIypk2bxi1PSkoSO26nTp24AI6IaHhSTTQ1NbnJn0VvzGqKHrc5OTliQfLS0lI8ffqUG1Je32vp1KlT0NHR4T6fPHkSurq63FCw6j0RRVq2bAlDQ0M8ePBArLe5pNXW1g0ZMgT37t2Djo5Ok7+sYdq0aXB3d8eOHTvg4eHBW7djxw4UFBRg2rRpXPkcPXoUM2fOrDFf76of/fv3x/bt2/Hnn39yL4ipqKhAUlIS+vfvz0t77949PHr0CJqamgCAR48e4fbt23BycgIAbhhzQUGB2FDSD01aWhrffvstfHx8eEHw/v3748SJE3jw4AE3+XdhYSEuXLjAnQfwJphga2uL+Ph49OrVC1VVVbCysuLWf8g25NSpU9yLGCorK5GUlAQDAwMA9f8u0hhlZWXw9/eHvLw8Jk2aBGVl5TqVwZAhQ7Bjxw5kZmbygn8iX2JZNhcUcCINZmJigt27d8Pf3x+Wlpa4fv06jh492qB9derUCQYGBoiMjETr1q0hKyuLn376Saw7s7GxMWJjY9GzZ0+oqKhg//79Yl0da6OqqgoLCwuuN4CCgkKD8iwpUlJS8PHxgZeXF0pKSmBmZgZFRUVkZmbizJkzWLhwIe/XnIsXL2LDhg0wMTHB+fPncfToUaxcuVLs1cJv+5LLuDp9ff0a3zgn0pjzNzY2hqmpKfz8/PD48WMYGBigoKAAJ06cQEhIiITO4NOSnJyMkpISuLq6YtCgQWLrt23bhvj4eCxatAhAw+svkWzdtbOzw6ZNm5Cfn//J9ED8UOp6L6wLZWVleHp6IigoCFlZWRg4cCBkZGSQnp6O5ORkhIeHQ1FRsQnO4tPT2HJ1dHTE7t27ISMjgwkTJjRxbptWXe+LkydPRufOnbF+/XosXrwYxcXFiI6O5n4hF3Fzc4O7uzuWL18Oa2tr3Lx5E7/++isA8O6dVlZW2L17N/r06QMtLS0cO3ZMbG5Ad3d36OrqQkdHB4qKikhJSUFhYSE3fKl79+6QkZFBXFwcZGVlISMj0+jgzNixY2Fubo6hQ4dCTU0Nz549w759+5Cfn88F0ep7LR09ehQKCgrQ0dFBYmIirly5gujoaG59jx49EBcXh/j4eGhqakJFRQUaGhpYsmQJpk2bhgULFmD06NFQVlZGVlYWLly4gAkTJtT492qI2to6T09PODg4YNasWXB0dET79u2Rk5ODy5cvw8jICGPGjJFIPoA38325uLggIiICWVlZ3I+AJ0+exC+//AIXFxcusLN48WJMnz4d06dPx5QpU9CmTRvcuHEDKioqcHBwQIcOHaCsrIyEhARoaGhAXl4eQqEQZmZm0NfXh7e3NxYvXoz27dtj7969eP78OcLCwnj5adeuHb799lt4enoCAEJDQ9GxY0fuutfS0oKzszOWLFmCWbNmwcDAAOXl5UhLS8OlS5fe2w42hbFjxyIyMhKXLl3ihvJNmDABu3btwty5c7FgwQK0aNECW7duhaysLC8wDLyZg3H79u0IDQ2FiYkJb7jlh2pD5OTkuOH1GhoaOHDgALKysrj5qer7XaSuqqqq8M8//wB405vu7t27iImJQXp6OgIDA7leR3Upg+nTp+Po0aNwcXGBm5sbunbtivT0dKSlpcHb2/uzL8vmjAJOpF5KS0u5ca6mpqbw8vLCvn37cPjwYfTr1w9RUVG8yH19bNq0CcuXL4evry/at2+PBQsWICEhgff67hUrVmDVqlXw9/eHoqIi7O3tYWlpieXLl9frWJaWljh+/Pgn88plGxsbKCsr48cff+R6iamrq2PYsGFivwp+//33iImJwYEDB9CqVSvMnz+/zhH0L7mM66qx5x8eHo6IiAjExMQgIiIC7dq1k9hrej9F8fHx6NKlyzsf0sePH4/169fj8ePHABpXf8n71afutm3bFgMHDkRWVhYMDQ0/fGY/sOptF1C3e2FdzZw5Ex07dsTOnTuxb98+yMrKolu3bjAzM6txOMjnRJLl2rNnT3Tv3h3dunXjDZVqjup6X3z69CkiIiKwevVqzJ8/H926dYOfnx8CAwN56S0sLLB69WpERUXh2LFjMDAwwOrVqzFz5kzexOru7u7Izc1FZGQkpKSk4OTkhKlTp/L2169fP/z+++/YuXMnKisroaWlhU2bNsHY2BjAmx+MVq5ciW3btuHYsWOoqKjgve67ITw8PJCSkoLAwEDk5eVBRUUFQqEQu3bt4s3TU59rafPmzdiyZQsiIyPRrl07+Pv78yaPdnBwQGpqKvz9/VFQUAB7e3sEBgaiX79+2L9/P8LDw+Hr64vy8nJ06tQJgwcP5nrcSEJtbZ2mpiZiY2MREhKCNWvWoKSkBB06dMCAAQMgFAollg+RFStWwMDAAPv378e8efMAvJlTLDAwkDeU0sjICHv27EFISAh8fX0hLS2NXr16YcGCBQDeBDgDAgKwZcsWTJ8+Ha9fv0ZycjI0NDQQHR2NjRs3IigoCCUlJdDV1cWOHTvEejjq6upi1KhRCAoKQnZ2NgwMDLBmzRrevWT58uXQ0tJCTEwMIiMj0apVK2hpaX2UHvMyMjKYM2cOry1VUlLC3r17ERgYiBUrVqCqqgr9+vXDvn370LlzZ972Ojo60NLSwsOHD+Hl5SW2/w/RhsjJyWHLli1Ys2YN7t69Cw0NDYSFhXHDGIH6fRepq9LSUq7HV8uWLaGhoYEhQ4YgIiKCN+F6XcpARUUFBw4cwObNm7Fp0ya8evUK6urqvGGpn3NZNmdSjDH2sTNBmg8PDw9kZmbi8OHDHzsrjbJkyRLcunWrwUMASe2ojEl1ly5dwtSpU3Ho0KEmH8pAaldcXIxhw4Zh3rx5mDlz5sfOTpP7XNquT40ky/Xx48cYNWoUQkNDG/zD1ZckNjYWy5cv577sfykOHz4MX19f/PXXX2ITcxNCCPn0UA8nUie3bt3C5cuXcfr0ae6Xkebozp07uHXrFhITE7Fq1aqPnZ3PEpUxIZ+u4uJi3L9/H/v374eUlFSzH7pUm8+l7frUSLJc8/Pz8fDhQ0RGRqJLly6wsLCQUC4/HwUFBYiIiMDgwYPRqlUr/Pfff/jxxx9hYWHxRQWbCCGEND8UcCJ14ufnh8LCQsyYMQOzZs362NlpMDc3N+Tl5WH8+PGYOHHix87OZ4nKmJBP140bNzB16lR07twZGzZsEJsr5nPzubRdnxpJlmtKSgr8/PygqamJoKAgyMrSo+nbZGVlkZ6ejvj4eLx48QIqKiqws7OrcXgOIYQQ8imhIXWEEEIIIYQQQgghRKLotUCEEEIIIYQQQgghRKIo4EQIIYQQQgghhBBCJIoCToQQQgghhBBCCCFEoijgRAghhBBCCCGEEEIkigJOhBBCCCFNQCgU4vvvv2/y47x8+RLLli2DiYkJhEIh1q1b1+TH/JjCw8MhFAqRl5f3sbNCCCGEkPeggBMhhBBCmpXDhw9DKBRCKBTi6tWrYusZYzA1NYVQKMTcuXObNC/Xrl1DeHg4ioqKmvQ47xMVFYUjR47g66+/xsaNG2FnZ/fOtCNGjGjyMiGEEEIIASjgRAghhJBmqkWLFoiPjxdbfvnyZWRlZUFeXr7J83D9+nVERER81IDTxYsXYWBgAA8PD9jZ2UFPT++j5YUQQgghRIQCToQQQghplkxNTXH8+HFUVFTwlsfHx0NXVxcdOnT4SDn7sHJzc6GsrPyxs0EIIYQQwkMBJ0IIIYQ0S6NHj0ZBQQHOnz/PLXv9+jVOnDiBsWPH1rhNSUkJAgMDYWpqCj09PVhZWWH79u1gjPHSieZfSkpKwpgxY6Cnp4fRo0fj7NmzXJrw8HBs3LgRAGBhYcEN83vy5AlvX+/bx/vk5ubCz88PxsbG6NOnD8aNG4cjR45w6y9dusQd7/Tp0+88fn1dvXoVnp6eMDMzg56eHkxNTbF+/XqUlpby0rm6usLV1VVsex8fH4wYMYL7/OTJEwiFQmzfvh0xMTEYOXIk9PT0MHHiRKSmpoptf//+fcyfPx+DBw+Gvr4+rKysEBwcLJbuxYsX8PHxgZGREfr37w9fX1+8evWqUedOCCGEEMmR/dgZIIQQQghpCHV1dRgaGiIhIQGmpqYAgLNnz+LFixewtbXF3r17eekZY3Bzc8OlS5fg4OCA3r1749y5c9i4cSOePXsGPz8/Xvq///4bJ0+exJQpU9CqVSvs3bsXnp6eSElJgYqKCiwtLZGWlob4+Hj4+vpCRUUFAKCqqlrnfbxLaWkpXF1d8fjxYzg7O0NDQwPHjx+Hj48PioqKMG3aNPTo0QMbN25EQEAAOnXqhBkzZogdvyGOHz+O0tJSfP3112jbti1SU1Oxb98+ZGVlISwsrMH7jY+Px8uXL+Hk5AQpKSls27YN8+bNQ1JSEuTk5AAAt2/fhrOzM2RlZeHk5AR1dXU8fvwYf/zxBxYuXMjb34IFC6ChoYFFixbh5s2biI2NhaqqKry9vRt1/oQQQgiRDAo4EUIIIaTZGjt2LDZv3ozS0lIoKCjgt99+w4ABA9CxY0extMnJybh48SIWLFgANzc3AICzszM8PT2xZ88euLi4oFu3blz6+/fvIzExkVs2aNAg2NnZISEhAS4uLtDW1oaOjg7i4+MxcuRIaGhoiB2ztn28S0xMDO7fv4+goCCMGzcOADB58mS4uroiJCQEEydORPv27WFnZ4fQ0FB07NjxvZOF14eXlxcUFBS4z05OTtDU1MSWLVuQmZmJLl26NGi/mZmZOHnyJNq0aQMA0NLSgru7O/7880+Ym5sDANauXQvGGI4cOcI7jpeXl9j+evfujfXr13OfCwoKcOjQIQo4EUIIIZ8IGlJHCCGEkGbLxsYGZWVlSElJQXFxMU6fPv3O4XRnz56FjIyM2DCwmTNngjEmNtTN2NiYF4DS1taGkpIS0tPT65y/hu7j7Nmz6NChA8aMGcMtk5OTg6urK0pKSnDlypU656G+qgebSkpKkJeXh759+4Ixhps3bzZ4v7a2tlywCQCMjIwAgCuLvLw8XLlyBRMnThQLaklJSYntb/LkybzPRkZGKCgoQHFxcYPzSAghhBDJoR5OhBBCCGm2VFVVMWTIEMTHx6O0tBSVlZWwsrKqMW1GRgbU1NSgpKTEW96jRw9ufXWdO3cW20ebNm3q9Ua6hu4jIyMDmpqakJbm/zYoymtmZmad81BfmZmZCAsLwx9//IHCwkLeusYEc94uC1HwSVQWosCTQCCo0/7eDkqJJk4vLCwU+xsTQggh5MOjgBMhhBBCmrUxY8ZgxYoVyMnJwfDhwyX2xjYZGZkal789wXhT7+NDqqysxIwZM1BYWIhvvvkGX331FVq2bIlnz57Bx8cHVVVVddpHTSRdFm8H4xq7P0IIIYRIFg2pI4QQQkizZmlpCWlpafzzzz+8IWhvU1dXx/Pnz8V66Tx48IBbX181DfWSBHV1dTx69EgswCPKa0PnUarN3bt3kZaWBh8fH8yZMwcjR46EsbEx1NTUxNK+q6dWQ3tfde3alcsDIYQQQpo/CjgRQgghpFlr1aoVVq9ejXnz5mHEiBHvTDd8+HBUVlbi559/5i3ftWsXpKSkMHz48HofW1FREQDw4sWLem/7PsOHD0d2djYSExO5ZRUVFdi7dy9atmyJAQMGSPR4IqJeQ9V7CTHGsGfPHrG0Xbt2xYMHD5CXl8ctu337Nq5du9agY6uqqmLAgAGIi4sTC1pRryVCCCGk+aEhdYQQQghp9uzt7WtNM2LECAwaNAjBwcHIyMiAUCjE+fPnkZycjGnTpvEm964rXV1dAEBwcDBsbW0hJycHc3NztGzZst77qs7JyQkxMTHw8fHBjRs3oK6ujhMnTuDatWvw8/Nr1BxFjx49wg8//CC2XEdHByYmJujWrRs2bNiAZ8+eQUlJCSdOnKixJ5ODgwN27dqFWbNmwcHBAbm5uTh48CB69uyJly9fNihvy5cvx9dffw17e3s4OTlBQ0MDGRkZOH36NI4ePdqgfRJCCCHk46CAEyGEEEK+CNLS0ti6dSvCwsKQmJiIw4cPQ11dHUuWLMHMmTMbtE99fX3Mnz8fBw8exLlz51BVVYXk5ORGB5wUFBSwd+9ebNq0CUeOHEFxcTG0tLQQEBCACRMmNGrfDx8+RGhoqNhyBwcHmJmZ4ccff8TatWsRFRWFFi1awNLSEs7OzrCzs+Ol79GjBzZs2ICwsDAEBASgZ8+e2LhxI+Lj43H58uUG5U1bWxu//PILQkNDceDAAZSVlaFLly6wsbFp0P4IIYQQ8vFIMeqjTAghhBBCCCGEEEIkiOZwIoQQQgghhBBCCCESRQEnQgghhBBCCCGEECJRFHAihBBCCCGEEEIIIRJFASdCCCGEEEIIIYQQIlEUcCKEEEIIIYQQQgghEkUBJ0IIIYQQQgghhBAiURRwIoQQQgghhBBCCCESRQEnQgghhBBCCCGEECJRFHAihBBCCCGEEEIIIRJFASdCCCGEEEIIIYQQIlEUcCKEEEIIIYQQQgghEkUBJ0IIIYQQQgghhBAiURRwIoQQQgghhBBCCCES9X+kLeMHuPGIwgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1400x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df = Data_Visualization.copy()\n",
        "df_success = df[df['state'] == 1]\n",
        "df_fail = df[df['state'] == 0]\n",
        "\n",
        "# Count number of successful and failed projects for each month\n",
        "success_counts = df_success.groupby('launched_at_month')['state'].count()\n",
        "fail_counts = df_fail.groupby('launched_at_month')['state'].count()\n",
        "\n",
        "import calendar\n",
        "months = [calendar.month_name[i] for i in range(1,13)]\n",
        "\n",
        "plt.figure(figsize=(14, 8))\n",
        "plt.bar(success_counts.index, success_counts.values, label='Successful')\n",
        "plt.bar(fail_counts.index, fail_counts.values, bottom=success_counts.values, label='Failed')\n",
        "\n",
        "plt.xlabel('Month of Launch')\n",
        "plt.ylabel('Number of Projects')\n",
        "\n",
        "plt.xticks(range(1,13), months)\n",
        "plt.title('Success vs. Failure by Month of Launch')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "id": "NlIX3pHuq5WE",
        "outputId": "757d797f-fa35-4636-d8e2-f392c16733a1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "subset = Data_Visualization[['category', 'pledged']]\n",
        "\n",
        "# group by category and sum the pledged amounts\n",
        "grouped = subset.groupby('category').sum()\n",
        "\n",
        "# sort the categories by the sum of pledged amounts in descending order and select the top eight\n",
        "top_categories = grouped.sort_values('pledged', ascending=False).head(8)\n",
        "total_pledged = top_categories['pledged'].sum()\n",
        "top_categories['percentage'] = top_categories['pledged'] / total_pledged * 100\n",
        "\n",
        "# create a bar plot of the top categories and their percentages\n",
        "ax = top_categories.plot(kind='bar', y='percentage', legend=False)\n",
        "ax.set_title('Percentage of Total Pledged Amount in Top 8 Categories')\n",
        "ax.set_xlabel('Category')\n",
        "ax.set_ylabel('Percentage')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "h4SanF6brR4M",
        "outputId": "75d13f4c-3d30-47a5-8c4c-c683d6354b98"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# group the data by deadline year and sum the pledged amounts for each year\n",
        "pledged_per_year = Data_Visualization.groupby('deadline_yr')['pledged'].sum()\n",
        "\n",
        "# plot a line graph of pledged amounts per year\n",
        "plt.plot(pledged_per_year.index, pledged_per_year.values)\n",
        "plt.title('Total Pledged Amount per Year')\n",
        "plt.xlabel('Year')\n",
        "plt.ylabel('Pledged Amount')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "HctkZME2Fe4l",
        "outputId": "e240b200-b5d9-48e3-f011-28e3f1cf4fed"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#This gdp values of united states is taken from worldbank data for analysis.\n",
        "gdp = [14.48,15.05,15.6,16.25, 16.84, 17.55, 18.21, 18.7, 19.48]\n",
        "\n",
        "pledged_per_year = Data_Visualization[Data_Visualization['country'] == 'US'].groupby('deadline_yr')['pledged'].sum()\n",
        "x = pledged_per_year.index\n",
        "\n",
        "y1 = pledged_per_year.values\n",
        "y2 = gdp\n",
        "\n",
        "fig, ax1 = plt.subplots()\n",
        "color = 'tab:red'\n",
        "ax1.set_xlabel('Year')\n",
        "ax1.set_ylabel('Pledged Amount (US) ', color=color)\n",
        "ax1.plot(x, y1, color=color)\n",
        "ax1.tick_params(axis='y', labelcolor=color)\n",
        "\n",
        "# create line plot for y2\n",
        "ax2 = ax1.twinx()  \n",
        "color = 'tab:blue'\n",
        "ax2.set_ylabel('GDP(US)', color=color)\n",
        "ax2.plot(x, y2, color=color)\n",
        "ax2.tick_params(axis='y', labelcolor=color)\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.legend(['GDP(US)', 'Pledged Amount (US) '])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvBLDYnmrrcx"
      },
      "source": [
        "# **Feature Extraction using RFE for Binary Classification**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YM9qpKcxkcpG"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "val_x = kickstarter_Binary.loc[:, ~kickstarter_Binary.columns.isin(['state'])]\n",
        "val_y = kickstarter_Binary['state']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5vhSMSm4ykEh",
        "outputId": "04d4b241-2cce-49e0-f8ea-2daf9fd31e6d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Score with selected features  1 is:  0.8365789894894093\n",
            "Model Score with selected features  2 is:  0.9938110227818385\n",
            "Model Score with selected features  3 is:  1.0\n",
            "Model Score with selected features  4 is:  1.0\n",
            "Model Score with selected features  5 is:  1.0\n",
            "Model Score with selected features  6 is:  1.0\n",
            "Model Score with selected features  7 is:  1.0\n",
            "Model Score with selected features  8 is:  1.0\n",
            "Model Score with selected features  9 is:  1.0\n",
            "Model Score with selected features  10 is:  1.0\n",
            "Model Score with selected features  11 is:  1.0\n",
            "Model Score with selected features  12 is:  1.0\n",
            "Model Score with selected features  13 is:  1.0\n",
            "Model Score with selected features  14 is:  1.0\n",
            "Model Score with selected features  15 is:  1.0\n",
            "Model Score with selected features  16 is:  1.0\n",
            "Model Score with selected features  17 is:  1.0\n",
            "Model Score with selected features  18 is:  1.0\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# Split the dataset into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(val_x, val_y, test_size=0.3, random_state=42)\n",
        "\n",
        "for i in range(1,len(X_train.columns)+1):\n",
        "\n",
        "  # Create a decision tree classifier\n",
        "  dt = DecisionTreeClassifier()\n",
        "\n",
        "  # Create RFE selector with decision tree classifier\n",
        "  selector = RFE(dt, n_features_to_select=i)\n",
        "  #print(selector)\n",
        "\n",
        "  # Fit the selector to the training data\n",
        "  fit = selector.fit(val_x, val_y)\n",
        "\n",
        "  score = selector.score(val_x, val_y)\n",
        "  print(\"Model Score with selected features \", i, \"is: \", score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bc0fxur4rybj",
        "outputId": "e8b43d88-b2c6-4ff0-b629-4342e45eca47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "RFE(estimator=DecisionTreeClassifier(), n_features_to_select=3)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.feature_selection import RFE\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "val_x = kickstarter_Binary.loc[:, ~kickstarter_Binary.columns.isin(['state'])]\n",
        "val_y = kickstarter_Binary['state']\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(val_x, val_y, test_size=0.3, random_state=1)\n",
        "\n",
        "# Create a decision tree classifier\n",
        "dt = DecisionTreeClassifier()\n",
        "\n",
        "# Create RFE selector with decision tree classifier\n",
        "selector = RFE(dt, n_features_to_select=3)\n",
        "print(selector)\n",
        "fit = selector.fit(val_x, val_y)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZDnCL_o0RY5",
        "outputId": "0be2631b-0243-4106-e7e2-68978cb14941"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Num Features: 3\n",
            "Selected Features: [ True  True False False False False  True False False False False False\n",
            " False False False False False False]\n",
            "Selected Features names: Index(['goal', 'pledged', 'backers_count'], dtype='object')\n",
            "Feature Ranking: [ 1  1  4 13 15 16  1  7  2 10  5 12  6 14  9 11  8  3]\n"
          ]
        }
      ],
      "source": [
        "selected_features = val_x.columns[fit.support_]\n",
        "print(\"Num Features:\", fit.n_features_)\n",
        "print(\"Selected Features:\", fit.support_)\n",
        "print(\"Selected Features names:\", selected_features)\n",
        "print(\"Feature Ranking:\", fit.ranking_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qxymBoy0Yt3",
        "outputId": "d3756394-abd3-40d6-b675-13b6cfc0b59e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model Score with selected features 3 is:  1.0\n"
          ]
        }
      ],
      "source": [
        "score = selector.score(val_x, val_y)\n",
        "print(\"Model Score with selected features 3 is: \", score)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t-dYcCKP0kD7"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Using RFE, with just three features we got model score as 1, hence we are using only those three features\n",
        "X_new = kickstarter_Binary[['goal', 'pledged', 'backers_count']]\n",
        "y_new = kickstarter_Binary['state']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fubsD3cp0qmV",
        "outputId": "51cd2145-e5ba-4e6a-8889-fcb4b6ac1719"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['goal', 'pledged', 'backers_count'], dtype='object')"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X_new.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xtteKeZ00yI8"
      },
      "outputs": [],
      "source": [
        "# Split the data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_new, y_new, test_size=0.3, random_state=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmhGpoRx03Bg"
      },
      "source": [
        "# **Machine Learning Models For Binary Classification**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qNq3LyU-06EJ"
      },
      "source": [
        "# **1. Logistic Regression**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yOa61c3209Yd"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "# instantiate the model (using the default parameters)\n",
        "logreg = LogisticRegression(random_state=1)\n",
        "\n",
        "# fit the model with data\n",
        "logreg.fit(X_train, y_train)\n",
        "\n",
        "y_pred = logreg.predict(X_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tiw_NbiH1EDf",
        "outputId": "7866f8c3-d5de-4514-cd5c-1d50bc737c38"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[3982,   52],\n",
              "       [   0, 1589]])"
            ]
          },
          "execution_count": 48,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uiJ737IP1F1k",
        "outputId": "41338879-bfce-4ae3-f696-997558189da2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      0.99      0.99      4034\n",
            "         1.0       0.97      1.00      0.98      1589\n",
            "\n",
            "    accuracy                           0.99      5623\n",
            "   macro avg       0.98      0.99      0.99      5623\n",
            "weighted avg       0.99      0.99      0.99      5623\n",
            "\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import accuracy_score\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l4l_OCH1M6O",
        "outputId": "4f374381-c9e7-446e-fc12-c9d736074cb2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9907522674728793\n"
          ]
        }
      ],
      "source": [
        "accuracy_log = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy_log)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-gFpqN31RxK"
      },
      "source": [
        "# **2. Decision Tree with depth 3**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M7Q3lwgk1S08"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.tree import DecisionTreeClassifier, export_graphviz\n",
        "# X_train, X_test, y_train, y_test = train_test_split(val_x, val_y, test_size=0.25, random_state=1)\n",
        "\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dtc_3 = DecisionTreeClassifier(max_depth = 3)\n",
        "dtc_3.fit(X_train, y_train)\n",
        "y_pred = dtc_3.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8mBeXEN1Wdw",
        "outputId": "caedb3fa-dc4b-4302-cdff-f8aba52a30ca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.89      0.93      4034\n",
            "         1.0       0.77      0.95      0.85      1589\n",
            "\n",
            "    accuracy                           0.90      5623\n",
            "   macro avg       0.87      0.92      0.89      5623\n",
            "weighted avg       0.92      0.90      0.91      5623\n",
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KGO_AEPQ1cBY",
        "outputId": "665b0588-b03e-4544-c26f-9b78bdbad642"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9046772185666014\n"
          ]
        }
      ],
      "source": [
        "accuracy_dtc = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy_dtc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 539
        },
        "id": "4TNvdcBB1jED",
        "outputId": "25e62279-3852-4e24-fbdb-8f2f5f3ff254"
      },
      "outputs": [
        {
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              "<text text-anchor=\"start\" x=\"481\" y=\"-319.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 13120</text>\n",
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              "<!-- 0&#45;&gt;8 -->\n",
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              "</svg>\n"
            ],
            "text/plain": [
              "<graphviz.sources.Source at 0x7f8b7e788100>"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import graphviz\n",
        "dot_data = export_graphviz(dtc_3, out_file=None, \n",
        "                           feature_names=X_train.columns.values, \n",
        "                           filled=True, rounded=True,  \n",
        "                           special_characters=True) \n",
        "\n",
        "# Render the Graphviz representation as a PNG image\n",
        "graph = graphviz.Source(dot_data)\n",
        "graph.format = 'png'\n",
        "graph.render('decision_tree')\n",
        "graph"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWGoKy1d1q3M"
      },
      "source": [
        "## **3. KNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e8wviE3Y1rm6"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier(n_neighbors=2)\n",
        "\n",
        "# train the classifier on the training set\n",
        "knn.fit(X_train, y_train)\n",
        "\n",
        "# make predictions on the testing set\n",
        "y_pred = knn.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZ9U6k-q1wTu",
        "outputId": "ea4acf84-7814-4b76-d088-d0992cc59757"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.99      0.98      4034\n",
            "         1.0       0.97      0.94      0.95      1589\n",
            "\n",
            "    accuracy                           0.97      5623\n",
            "   macro avg       0.97      0.96      0.97      5623\n",
            "weighted avg       0.97      0.97      0.97      5623\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wt64VWR1x8O",
        "outputId": "a8f9daa3-aa4a-4cb7-e7ee-63886d99a68f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9743908945402809\n"
          ]
        }
      ],
      "source": [
        "accuracy_knn = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy_knn)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Y5gZdVM15GE"
      },
      "source": [
        "# **4. Random Forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p2snXh5X12fy"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc = RandomForestClassifier(n_estimators=100, random_state=1)\n",
        "\n",
        "rfc.fit(X_train, y_train)\n",
        "\n",
        "# make predictions on the testing set\n",
        "y_pred = rfc.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vBAfE8WT1-8b",
        "outputId": "a25e362e-1616-4131-c3f1-48777f74897a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       1.00      0.98      0.99      4034\n",
            "         1.0       0.96      0.99      0.97      1589\n",
            "\n",
            "    accuracy                           0.99      5623\n",
            "   macro avg       0.98      0.99      0.98      5623\n",
            "weighted avg       0.99      0.99      0.99      5623\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QXG7nOic2LHf",
        "outputId": "ef178974-172d-4b6e-daa9-9158f81fa83d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9850613551484972\n"
          ]
        }
      ],
      "source": [
        "accuracy_rf = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy_rf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rx5NrP4iHvWC"
      },
      "source": [
        "# **5. SVM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzHQR-FFHybp",
        "outputId": "9ebb6b64-ef3c-4294-fdae-c61cc0aeb943"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.7983282945047128\n"
          ]
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "svc = SVC(kernel='rbf')\n",
        "\n",
        "# Train the SVM model\n",
        "svc.fit(X_train, y_train)\n",
        "\n",
        "# Predict the labels of the test data\n",
        "y_pred = svc.predict(X_test)\n",
        "\n",
        "accuracy_svm = accuracy_score(y_test, y_pred)\n",
        "print('Accuracy:', accuracy_svm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jTzda37wzqYM",
        "outputId": "ad1ff3e4-fc2e-4d1a-f030-cd1afd59e04d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.79      0.99      0.88      4034\n",
            "         1.0       0.92      0.31      0.47      1589\n",
            "\n",
            "    accuracy                           0.80      5623\n",
            "   macro avg       0.85      0.65      0.67      5623\n",
            "weighted avg       0.82      0.80      0.76      5623\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbcqVBQP2QYu"
      },
      "source": [
        "# **Accuracies before Cross validation For Binary Classification**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "k2C4HHn-2Okl",
        "outputId": "78dbe8da-d0a2-4654-c5c0-c311346f14ff"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "accuracy = [accuracy_log, accuracy_dtc, accuracy_knn, accuracy_rf, accuracy_svm]\n",
        "labels = [\"Logistic Regression\", \"Decision Tree\",\"KNN\", \"Random Forest\",\"SVM\"]\n",
        "plt.plot(labels, accuracy)\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Model Accuracies')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K2vwAXKD2dO4"
      },
      "source": [
        "# **Using Cross Validation For Binary Classification**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "NuQ6JSMd2eMU",
        "outputId": "86b0c3dd-0874-480c-df97-4d150373db09"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest Accuracy: 0.97 (+/- 0.01)\n",
            "Logistic Accuracy: 0.87 (+/- 0.07)\n",
            "K-Nearest Neighbors Accuracy: 0.97 (+/- 0.01)\n",
            "Decision Tree Accuracy: 0.90 (+/- 0.03)\n",
            "SVM Accuracy: 0.80 (+/- 0.05)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create a dictionary of classifiers\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "classifiers = {'Random Forest': RandomForestClassifier(n_estimators=100),\n",
        "               'Logistic': LogisticRegression(random_state=1),\n",
        "               'K-Nearest Neighbors': KNeighborsClassifier(n_neighbors=2),\n",
        "               'Decision Tree': DecisionTreeClassifier(max_depth = 3),\n",
        "               'SVM' : SVC(kernel = 'rbf')}\n",
        "scores = {}\n",
        "for clf_name, clf in classifiers.items():\n",
        "    cv_scores = cross_val_score(clf, val_x, val_y, cv=5)\n",
        "    scores[clf_name] = cv_scores\n",
        "    print(f\"{clf_name} Accuracy: {np.mean(cv_scores):.2f} (+/- {np.std(cv_scores):.2f})\")\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "ax.boxplot(scores.values())\n",
        "ax.set_xticklabels(scores.keys())\n",
        "ax.set_title('Classifier Comparison')\n",
        "ax.set_ylabel('Accuracy')\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}