Srishti Chandra_Project-1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPrbRsXE851k5EATASPxfCV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Srishti2703/Fake_News_Classifier/blob/main/Srishti%20Chandra_Project-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4ANSdIhfMtfy",
        "outputId": "b2fae0dd-551e-4eef-82b0-51398827c82a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-63fc3de835f8>:45: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(y='Preferred_Activities', data=df_customers, palette='viridis')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-1-63fc3de835f8>:53: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(x='Preferred_Destination_Type', data=df_customers, palette='coolwarm')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   CustomerID  Budget Preferred_Activities Preferred_Destination_Type  \\\n",
            "0           1    1360               Nature                Countryside   \n",
            "1           2    4272              Culture                       City   \n",
            "2           3    3592             Shopping                   Mountain   \n",
            "3           4     966              Culture                   Mountain   \n",
            "4           5    4926             Shopping                       City   \n",
            "5           6    3944               Nature                   Mountain   \n",
            "6           7    3671            Adventure                       City   \n",
            "7           8    3419             Shopping                Countryside   \n",
            "8           9     630              Culture                       City   \n",
            "9          10    2185               Nature                Countryside   \n",
            "\n",
            "  Recommended_Destination  Estimated_Cost  \n",
            "0              Swiss Alps          4000.0  \n",
            "1                New York          1050.0  \n",
            "2                    None             NaN  \n",
            "3                New York          2450.0  \n",
            "4                    None             NaN  \n",
            "5              Swiss Alps          2800.0  \n",
            "6                    Bali          1250.0  \n",
            "7                    None             NaN  \n",
            "8                New York          3500.0  \n",
            "9              Swiss Alps          4000.0  \n",
            "   CustomerID  Budget Preferred_Activities Preferred_Destination_Type  \\\n",
            "0           1    1360               Nature                Countryside   \n",
            "1           2    4272              Culture                       City   \n",
            "2           3    3592             Shopping                   Mountain   \n",
            "3           4     966              Culture                   Mountain   \n",
            "4           5    4926             Shopping                       City   \n",
            "5           6    3944               Nature                   Mountain   \n",
            "6           7    3671            Adventure                       City   \n",
            "7           8    3419             Shopping                Countryside   \n",
            "8           9     630              Culture                       City   \n",
            "9          10    2185               Nature                Countryside   \n",
            "\n",
            "  Optimized_Recommended_Destination  Optimized_Estimated_Cost  \n",
            "0                              None                       NaN  \n",
            "1                          New York                    1050.0  \n",
            "2                              None                       NaN  \n",
            "3                              None                       NaN  \n",
            "4                              None                       NaN  \n",
            "5                        Swiss Alps                    2800.0  \n",
            "6                              Bali                    1250.0  \n",
            "7                              None                       NaN  \n",
            "8      New York (Reduced to 1 days)                     350.0  \n",
            "9                              None                       NaN  \n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Data Collection\n",
        "np.random.seed(42)\n",
        "\n",
        "# Sample data for 10 customers\n",
        "data = {\n",
        "    'CustomerID': range(1, 11),\n",
        "    'Budget': np.random.randint(500, 5000, 10),\n",
        "    'Preferred_Activities': np.random.choice(['Adventure', 'Culture', 'Relaxation', 'Shopping', 'Nature'], 10),\n",
        "    'Preferred_Travel_Duration': np.random.choice(['3 Days', '5 Days', '7 Days', '10 Days'], 10),\n",
        "    'Preferred_Destination_Type': np.random.choice(['Beach', 'Mountain', 'City', 'Countryside'], 10)\n",
        "}\n",
        "\n",
        "# Creating a DataFrame for customers\n",
        "df_customers = pd.DataFrame(data)\n",
        "\n",
        "# Sample data for destinations\n",
        "destinations = {\n",
        "    'DestinationID': range(1, 6),\n",
        "    'Destination_Name': ['Bali', 'Swiss Alps', 'New York', 'Santorini', 'Kyoto'],\n",
        "    'Activity_Type': ['Adventure', 'Nature', 'Culture', 'Relaxation', 'Culture'],\n",
        "    'Destination_Type': ['Beach', 'Mountain', 'City', 'Beach', 'City'],\n",
        "    'Cost_Per_Day': [250, 400, 350, 300, 280]\n",
        "}\n",
        "\n",
        "# Creating a DataFrame for destinations\n",
        "df_destinations = pd.DataFrame(destinations)\n",
        "\n",
        "# Exploratory Data Analysis (EDA)\n",
        "\n",
        "# Distribution of customer budgets\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.histplot(df_customers['Budget'], bins=10, kde=True, color='skyblue')\n",
        "plt.title('Distribution of Customer Budgets')\n",
        "plt.xlabel('Budget')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()\n",
        "\n",
        "# Preferred Activities\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(y='Preferred_Activities', data=df_customers, palette='viridis')\n",
        "plt.title('Preferred Activities of Customers')\n",
        "plt.xlabel('Number of Customers')\n",
        "plt.ylabel('Preferred Activities')\n",
        "plt.show()\n",
        "\n",
        "# Preferred Destination Type\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='Preferred_Destination_Type', data=df_customers, palette='coolwarm')\n",
        "plt.title('Preferred Destination Types')\n",
        "plt.xlabel('Preferred Destination Type')\n",
        "plt.ylabel('Number of Customers')\n",
        "plt.show()\n",
        "\n",
        "# Simple Recommendation Model\n",
        "\n",
        "# Function to recommend a destination based on customer preferences\n",
        "def recommend_destination(customer):\n",
        "    # Filter destinations based on preferred activity and destination type\n",
        "    filtered_destinations = df_destinations[\n",
        "        (df_destinations['Activity_Type'] == customer['Preferred_Activities']) &\n",
        "        (df_destinations['Destination_Type'] == customer['Preferred_Destination_Type'])\n",
        "    ]\n",
        "\n",
        "    # Check if any destination matches\n",
        "    if not filtered_destinations.empty:\n",
        "        # Check if the customer budget allows for the selected destination\n",
        "        for _, dest in filtered_destinations.iterrows():\n",
        "            total_cost = dest['Cost_Per_Day'] * int(customer['Preferred_Travel_Duration'].split()[0])\n",
        "            if total_cost <= customer['Budget']:\n",
        "                return dest['Destination_Name'], total_cost\n",
        "\n",
        "    # If no match found within budget, recommend the closest option based on activity preference\n",
        "    closest_match = df_destinations[\n",
        "        (df_destinations['Activity_Type'] == customer['Preferred_Activities'])\n",
        "    ]\n",
        "\n",
        "    if not closest_match.empty:\n",
        "        dest = closest_match.iloc[0]\n",
        "        total_cost = dest['Cost_Per_Day'] * int(customer['Preferred_Travel_Duration'].split()[0])\n",
        "        return dest['Destination_Name'], total_cost\n",
        "\n",
        "    # If no close match found, return None\n",
        "    return None, None\n",
        "\n",
        "# Apply the recommendation function to each customer\n",
        "df_customers['Recommended_Destination'], df_customers['Estimated_Cost'] = zip(\n",
        "    *df_customers.apply(recommend_destination, axis=1)\n",
        ")\n",
        "\n",
        "# Displaying the updated customer dataframe with recommendations\n",
        "print(df_customers[['CustomerID', 'Budget', 'Preferred_Activities', 'Preferred_Destination_Type',\n",
        "                   'Recommended_Destination', 'Estimated_Cost']])\n",
        "\n",
        "# Optimized Recommendation Model\n",
        "\n",
        "# Function to recommend a destination with optimization\n",
        "def optimized_recommendation(customer):\n",
        "    \"\"\"\n",
        "    Recommends a travel destination based on customer preferences and budget.\n",
        "    If no direct match is found, considers alternative destination types or reduces travel duration.\n",
        "\n",
        "    Parameters:\n",
        "    customer (pd.Series): A row from the customer DataFrame.\n",
        "\n",
        "    Returns:\n",
        "    str, float: Recommended destination name and estimated cost, or None if no match is found.\n",
        "    \"\"\"\n",
        "    # Filter destinations by preferred activity and destination type\n",
        "    filtered_destinations = df_destinations[\n",
        "        (df_destinations['Activity_Type'] == customer['Preferred_Activities']) &\n",
        "        (df_destinations['Destination_Type'] == customer['Preferred_Destination_Type'])\n",
        "    ]\n",
        "\n",
        "    # Check if any destination matches within budget\n",
        "    for _, dest in filtered_destinations.iterrows():\n",
        "        total_cost = dest['Cost_Per_Day'] * int(customer['Preferred_Travel_Duration'].split()[0])\n",
        "        if total_cost <= customer['Budget']:\n",
        "            return dest['Destination_Name'], total_cost\n",
        "\n",
        "    # If no match found, try alternative destination types with the same activity\n",
        "    alternative_destinations = df_destinations[\n",
        "        (df_destinations['Activity_Type'] == customer['Preferred_Activities'])\n",
        "    ]\n",
        "\n",
        "    for _, dest in alternative_destinations.iterrows():\n",
        "        total_cost = dest['Cost_Per_Day'] * int(customer['Preferred_Travel_Duration'].split()[0])\n",
        "        if total_cost <= customer['Budget']:\n",
        "            return dest['Destination_Name'], total_cost\n",
        "\n",
        "    # If still no match, suggest reducing the travel duration to fit within the budget\n",
        "    for _, dest in filtered_destinations.iterrows():\n",
        "        adjusted_duration = customer['Budget'] // dest['Cost_Per_Day']\n",
        "        if adjusted_duration >= 1:  # Ensure at least 1 day is affordable\n",
        "            return f\"{dest['Destination_Name']} (Reduced to {adjusted_duration} days)\", dest['Cost_Per_Day'] * adjusted_duration\n",
        "\n",
        "    # If no match found even after all optimizations\n",
        "    return None, None\n",
        "\n",
        "# Apply the optimized recommendation function to each customer\n",
        "df_customers['Optimized_Recommended_Destination'], df_customers['Optimized_Estimated_Cost'] = zip(\n",
        "    *df_customers.apply(optimized_recommendation, axis=1)\n",
        ")\n",
        "\n",
        "# Displaying the updated customer dataframe with optimized recommendations\n",
        "print(df_customers[['CustomerID', 'Budget', 'Preferred_Activities', 'Preferred_Destination_Type',\n",
        "                   'Optimized_Recommended_Destination', 'Optimized_Estimated_Cost']])"
      ]
    }
  ]
}