Online_Retail_Customer_Segmentation.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/M123shashank/CP3_Online-Retail-Customer-Segmentation/blob/main/Online_Retail_Customer_Segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Name**    -\n",
        "\n"
      ],
      "metadata": {
        "id": "vncDsAP0Gaoa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **Project Type**    - EDA/Regression/Classification/Unsupervised\n",
        "##### **Contribution**    - Individual/Team\n",
        "##### **Team Member 1 -**\n",
        "##### **Team Member 2 -**\n",
        "##### **Team Member 3 -**\n",
        "##### **Team Member 4 -**"
      ],
      "metadata": {
        "id": "beRrZCGUAJYm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Summary -**"
      ],
      "metadata": {
        "id": "FJNUwmbgGyua"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Customer segmentation, often referred to as market segmentation, is the process of dividing a company's customers into groups or segments based on specific characteristics. The goal of this division is to tailor marketing efforts, products, services, and sometimes even pricing to the specific needs and preferences of each segment.\n",
        "\n",
        "The main reasons for segmenting customers are:\n",
        "1. **Better Targeting**: By understanding the different segments, businesses can target their marketing messages to resonate more powerfully with specific groups, leading to better marketing efficiency and effectiveness.\n",
        "2. **Product Development**: Helps businesses create products or services that cater to the unique needs of each segment.\n",
        "3. **Improved Customer Service**: Businesses can tailor their service to meet the unique needs and preferences of each group.\n",
        "4. **Optimal Pricing**: Enables businesses to set different price points based on the perceived value by each segment.\n",
        "5. **Enhanced Customer Retention**: Understanding the varied needs of segments can help businesses better cater to those needs and thus retain customers.\n",
        "\n",
        "Let us divide our customers based on three factors for this project:\n",
        "1. Geographics:- It represents the location (country), where customers are more likely to purchase from my company.\n",
        "2. Recency:- It represents how recently a customer purchased a product.\n",
        "3. Frequency:- It represents how often a customer purchased a product. The more frequent will be the better score.\n",
        "4. Monetary:- It represents how much an customer spends.\n",
        "\n",
        "By understanding and applying customer segmentation, businesses can more effectively allocate their resources and tailor their offerings to the most relevant and profitable groups."
      ],
      "metadata": {
        "id": "F6v_1wHtG2nS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **GitHub Link -**"
      ],
      "metadata": {
        "id": "w6K7xa23Elo4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Provide your GitHub Link here."
      ],
      "metadata": {
        "id": "h1o69JH3Eqqn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Problem Statement**\n"
      ],
      "metadata": {
        "id": "yQaldy8SH6Dl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***In this project, our task is to identify major customer segments on a transnational\n",
        "data set which contains all the transactions occurring between 01/12/2010 and\n",
        "09/12/2011 for a UK-based and registered non-store online retail.The company\n",
        "mainly sells unique all-occasion gifts. Many customers of the company are\n",
        "wholesalers.***"
      ],
      "metadata": {
        "id": "DpeJGUA3kjGy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **General Guidelines** : -  "
      ],
      "metadata": {
        "id": "mDgbUHAGgjLW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1.   Well-structured, formatted, and commented code is required.\n",
        "2.   Exception Handling, Production Grade Code & Deployment Ready Code will be a plus. Those students will be awarded some additional credits.\n",
        "     \n",
        "     The additional credits will have advantages over other students during Star Student selection.\n",
        "       \n",
        "             [ Note: - Deployment Ready Code is defined as, the whole .ipynb notebook should be executable in one go\n",
        "                       without a single error logged. ]\n",
        "\n",
        "3.   Each and every logic should have proper comments.\n",
        "4. You may add as many number of charts you want. Make Sure for each and every chart the following format should be answered.\n",
        "        \n",
        "\n",
        "```\n",
        "# Chart visualization code\n",
        "```\n",
        "            \n",
        "\n",
        "*   Why did you pick the specific chart?\n",
        "*   What is/are the insight(s) found from the chart?\n",
        "* Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason.\n",
        "\n",
        "5. You have to create at least 15 logical & meaningful charts having important insights.\n",
        "\n",
        "\n",
        "[ Hints : - Do the Vizualization in  a structured way while following \"UBM\" Rule.\n",
        "\n",
        "U - Univariate Analysis,\n",
        "\n",
        "B - Bivariate Analysis (Numerical - Categorical, Numerical - Numerical, Categorical - Categorical)\n",
        "\n",
        "M - Multivariate Analysis\n",
        " ]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "6. You may add more ml algorithms for model creation. Make sure for each and every algorithm, the following format should be answered.\n",
        "\n",
        "\n",
        "*   Explain the ML Model used and it's performance using Evaluation metric Score Chart.\n",
        "\n",
        "\n",
        "*   Cross- Validation & Hyperparameter Tuning\n",
        "\n",
        "*   Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart.\n",
        "\n",
        "*   Explain each evaluation metric's indication towards business and the business impact pf the ML model used.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ZrxVaUj-hHfC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***Let's Begin !***"
      ],
      "metadata": {
        "id": "O_i_v8NEhb9l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***1. Know Your Data***"
      ],
      "metadata": {
        "id": "HhfV-JJviCcP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import Libraries"
      ],
      "metadata": {
        "id": "Y3lxredqlCYt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "M8Vqi-pPk-HR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Loading"
      ],
      "metadata": {
        "id": "3RnN4peoiCZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Dataset\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "4CkvbW_SlZ_R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/AlmaBetter/ML Projects/3 Unsupervised - Online Retail Customer Segmentation/Online Retail.csv')"
      ],
      "metadata": {
        "id": "IT5XSOwAeENk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset First View"
      ],
      "metadata": {
        "id": "x71ZqKXriCWQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset First Look\n",
        "df.head(5)"
      ],
      "metadata": {
        "id": "LWNFOSvLl09H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   We can observe that the dataset consists of all type of values such as, numerical, alphabetic, alphanumeric, date time, float, etc.\n",
        "2.   There are total 8 columns given in the dataset.\n",
        "\n"
      ],
      "metadata": {
        "id": "77PLAgD1gQeB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Rows & Columns count"
      ],
      "metadata": {
        "id": "7hBIi_osiCS2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Rows & Columns count\n",
        "df.shape"
      ],
      "metadata": {
        "id": "Kllu7SJgmLij"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Dataset Information"
      ],
      "metadata": {
        "id": "JlHwYmJAmNHm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Info\n",
        "df.info()"
      ],
      "metadata": {
        "id": "e9hRXRi6meOf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   We have to convert datatype of 'InvoiceDate' into datetime.\n",
        "\n"
      ],
      "metadata": {
        "id": "Sb1vn6XPqjfN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#conversion to datetime\n",
        "df[\"InvoiceDate\"] = pd.to_datetime(df[\"InvoiceDate\"])"
      ],
      "metadata": {
        "id": "S_KK6qPnrBKz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "-YsI4XYirTj4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let us check for outliers\n",
        "df.describe(percentiles=[.01,.25,.50,.75,.80,.99]).T"
      ],
      "metadata": {
        "id": "UL8tQ-7JlrD_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Looks like 'Quantity' and 'UnitPrice' have outliers at the extremes.\n",
        "2.   It can be observed that 'Description' and 'CustomerID' have missing values.\n",
        "\n"
      ],
      "metadata": {
        "id": "7YlYESu1nai2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Duplicate Values"
      ],
      "metadata": {
        "id": "35m5QtbWiB9F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Duplicate Value Count\n",
        "duplicate_rows = df[df.duplicated()]\n",
        "duplicate_rows.count()"
      ],
      "metadata": {
        "id": "1sLdpKYkmox0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "duplicate_rows"
      ],
      "metadata": {
        "id": "gMdXx83Sjsbw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   We can easily observe that all the columns can have dupliacte values which is not an issue.\n",
        "2.   We were in doubt regarding the 'Stock code' that whether it can be same for multiple goods. But after reasearch, we came to the conclusion that multiple goods of same category can have unique stock code.\n",
        "\n"
      ],
      "metadata": {
        "id": "xAwMsgbxl_34"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Missing Values/Null Values"
      ],
      "metadata": {
        "id": "PoPl-ycgm1ru"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values/Null Values Count\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "GgHWkxvamxVg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing the missing values\n",
        "plt.figure(figsize=(12, 20))\n",
        "sns.heatmap(df.isnull(), cbar=False, cmap='viridis', yticklabels=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "3q5wnI3om9sJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What did you know about your dataset?"
      ],
      "metadata": {
        "id": "H0kj-8xxnORC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "1.   Dataset has some missing values for two columns.\n",
        "2.   Dataset has outliers related to two columns.\n",
        "3.   Dataset have no column with invalid duplicate values.\n",
        "\n"
      ],
      "metadata": {
        "id": "gfoNAAC-nUe_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***2. Understanding Your Variables***"
      ],
      "metadata": {
        "id": "nA9Y7ga8ng1Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Columns\n",
        "df.keys()"
      ],
      "metadata": {
        "id": "j7xfkqrt5Ag5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Describe\n",
        "df.describe().T"
      ],
      "metadata": {
        "id": "DnOaZdaE5Q5t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Variables Description"
      ],
      "metadata": {
        "id": "PBTbrJXOngz2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Invoice NO  :   Invoice number\n",
        "\n",
        "Stock Code  :   Stock name code\n",
        "\n",
        "Description :   Description of product\n",
        "\n",
        "Quantity    :   Quantity bought\n",
        "\n",
        "InvoiceDate :   Invoice date\n",
        "\n",
        "UnitPrice   :   Price per unit\n",
        "\n",
        "CustomeriD  :   Unique customer id\n",
        "\n",
        "Country     :   location"
      ],
      "metadata": {
        "id": "aJV4KIxSnxay"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Check Unique Values for each variable."
      ],
      "metadata": {
        "id": "u3PMJOP6ngxN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Unique Values for each variable.\n",
        "\n",
        "unique_val = df.nunique()\n",
        "print(unique_val)"
      ],
      "metadata": {
        "id": "zms12Yq5n-jE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Country'].unique()"
      ],
      "metadata": {
        "id": "a2u8-OpGtSFC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "THSmp6BL4t1_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  These are the names of those 38 unique countries from where our customers approached us.\n",
        "\n"
      ],
      "metadata": {
        "id": "bpUpEuBptasw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. ***Data Wrangling***"
      ],
      "metadata": {
        "id": "dauF4eBmngu3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Wrangling Code"
      ],
      "metadata": {
        "id": "bKJF3rekwFvQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.max_rows', None)"
      ],
      "metadata": {
        "id": "yc8RBoeN6L7B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Write your code to make your dataset analysis ready.\n",
        "# Cheking the unique values of the InvoiceNo\n",
        "new = pd.DataFrame(df['InvoiceNo'].unique())\n",
        "new"
      ],
      "metadata": {
        "id": "wk-9a2fpoLcV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*  We can observe that some Invoice no. contains 'C' at the start which most likely to denote that certain order has been cancelled.\n",
        "*  Let us remove those rows.\n",
        "\n"
      ],
      "metadata": {
        "id": "Za6FBQEx9mEb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#changing datatype\n",
        "df['InvoiceNo'] = df['InvoiceNo'].astype('str')"
      ],
      "metadata": {
        "id": "WvH7RFjk4Wky"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping InvoiceNo starting with 'C'\n",
        "df=df[~df['InvoiceNo'].str.contains('C')]"
      ],
      "metadata": {
        "id": "A2gcJVDt-Rem"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "rrhS2_uq-2e6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### What all manipulations have you done and insights you found?"
      ],
      "metadata": {
        "id": "MSa1f5Uengrz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "LbyXE7I1olp8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***4. Data Vizualization, Storytelling & Experimenting with charts : Understand the relationships between variables***"
      ],
      "metadata": {
        "id": "GF8Ens_Soomf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 1"
      ],
      "metadata": {
        "id": "0wOQAZs5pc--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 1 visualization code\n",
        "des_df=df['Description'].value_counts().reset_index()\n",
        "des_df.rename(columns={'index': 'Description_Name','Description': 'Count'}, inplace=True)\n",
        "\n",
        "#top 5 Description Name\n",
        "des_df.head()"
      ],
      "metadata": {
        "id": "7v_ESjsspbW7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#plot top 5 product name\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.title('Top 5 Product Name')\n",
        "sns.barplot(x='Count',y='Description_Name',data=des_df[:5], palette='spring_r');"
      ],
      "metadata": {
        "id": "AL1y25VSB_5X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "K5QZ13OEpz2H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*I wanted to have a bivariate analysis between Product and its count. The barplot is best chart to do so.*"
      ],
      "metadata": {
        "id": "XESiWehPqBRc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "lQ7QKXXCp7Bj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The above chart gave us the top 5 products based on their count.**\n",
        "\n",
        "1. WHITE HANGING HEART T-LIGHT HOLDER\t2327\n",
        "2. JUMBO BAG RED RETROSPOT\t2115\n",
        "3. REGENCY CAKESTAND 3 TIER\t2019\n",
        "4. PARTY BUNTING\t1707\n",
        "5. LUNCH BAG RED RETROSPOT"
      ],
      "metadata": {
        "id": "C_j1G7yiqdRP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "448CDAPjqfQr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Yes. The above insight will create a positive impact as we can focus more on those top 5 products to increase our revenue."
      ],
      "metadata": {
        "id": "3cspy4FjqxJW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 2"
      ],
      "metadata": {
        "id": "KSlN3yHqYklG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 2 visualization code\n",
        "#bottom 5 description name\n",
        "des_df.tail()"
      ],
      "metadata": {
        "id": "R4YgtaqtYklH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "t6dVpIINYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "5aaW0BYyYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "ijmpgYnKYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "PSx9atu2YklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "-JiQyfWJYklI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "BcBbebzrYklV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 3"
      ],
      "metadata": {
        "id": "EM7whBJCYoAo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 3 visualization code"
      ],
      "metadata": {
        "id": "t6GMdE67YoAp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "fge-S5ZAYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "5dBItgRVYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "85gYPyotYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "4jstXR6OYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "RoGjAbkUYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "zfJ8IqMcYoAp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 4"
      ],
      "metadata": {
        "id": "4Of9eVA-YrdM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 4 visualization code"
      ],
      "metadata": {
        "id": "irlUoxc8YrdO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "iky9q4vBYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "aJRCwT6DYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "F6T5p64dYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "Xx8WAJvtYrdO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "y-Ehk30pYrdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "jLNxxz7MYrdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 5"
      ],
      "metadata": {
        "id": "bamQiAODYuh1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 5 visualization code"
      ],
      "metadata": {
        "id": "TIJwrbroYuh3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "QHF8YVU7Yuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "dcxuIMRPYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "GwzvFGzlYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "uyqkiB8YYuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "qYpmQ266Yuh3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "_WtzZ_hCYuh4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 6"
      ],
      "metadata": {
        "id": "OH-pJp9IphqM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 6 visualization code"
      ],
      "metadata": {
        "id": "kuRf4wtuphqN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "bbFf2-_FphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "loh7H2nzphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "_ouA3fa0phqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "VECbqPI7phqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "Seke61FWphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "DW4_bGpfphqN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 7"
      ],
      "metadata": {
        "id": "PIIx-8_IphqN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 7 visualization code"
      ],
      "metadata": {
        "id": "lqAIGUfyphqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "t27r6nlMphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "iv6ro40sphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "r2jJGEOYphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "Po6ZPi4hphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "b0JNsNcRphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "xvSq8iUTphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 8"
      ],
      "metadata": {
        "id": "BZR9WyysphqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 8 visualization code"
      ],
      "metadata": {
        "id": "TdPTWpAVphqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "jj7wYXLtphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "Ob8u6rCTphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "eZrbJ2SmphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "mZtgC_hjphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "rFu4xreNphqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "ey_0qi68phqO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 9"
      ],
      "metadata": {
        "id": "YJ55k-q6phqO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 9 visualization code"
      ],
      "metadata": {
        "id": "B2aS4O1ophqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "gCFgpxoyphqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "TVxDimi2phqP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "OVtJsKN_phqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "ngGi97qjphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "lssrdh5qphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "tBpY5ekJphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 10"
      ],
      "metadata": {
        "id": "U2RJ9gkRphqQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 10 visualization code"
      ],
      "metadata": {
        "id": "GM7a4YP4phqQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "1M8mcRywphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "8agQvks0phqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "tgIPom80phqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "Qp13pnNzphqQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "JMzcOPDDphqR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "R4Ka1PC2phqR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 11"
      ],
      "metadata": {
        "id": "x-EpHcCOp1ci"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 11 visualization code"
      ],
      "metadata": {
        "id": "mAQTIvtqp1cj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "X_VqEhTip1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "-vsMzt_np1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "8zGJKyg5p1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "ZYdMsrqVp1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "PVzmfK_Ep1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "druuKYZpp1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 12"
      ],
      "metadata": {
        "id": "n3dbpmDWp1ck"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 12 visualization code"
      ],
      "metadata": {
        "id": "bwevp1tKp1ck"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "ylSl6qgtp1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "m2xqNkiQp1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "ZWILFDl5p1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "x-lUsV2mp1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "M7G43BXep1ck"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "5wwDJXsLp1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 13"
      ],
      "metadata": {
        "id": "Ag9LCva-p1cl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Chart - 13 visualization code"
      ],
      "metadata": {
        "id": "EUfxeq9-p1cl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "E6MkPsBcp1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "V22bRsFWp1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "2cELzS2fp1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "ozQPc2_Ip1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact?\n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ],
      "metadata": {
        "id": "3MPXvC8up1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "GL8l1tdLp1cl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 14 - Correlation Heatmap"
      ],
      "metadata": {
        "id": "NC_X3p0fY2L0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation Heatmap visualization code"
      ],
      "metadata": {
        "id": "xyC9zolEZNRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "UV0SzAkaZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "DVPuT8LYZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "YPEH6qLeZNRQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "bfSqtnDqZNRR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Chart - 15 - Pair Plot"
      ],
      "metadata": {
        "id": "q29F0dvdveiT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Pair Plot visualization code"
      ],
      "metadata": {
        "id": "o58-TEIhveiU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ],
      "metadata": {
        "id": "EXh0U9oCveiU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "eMmPjTByveiU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ],
      "metadata": {
        "id": "22aHeOlLveiV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here"
      ],
      "metadata": {
        "id": "uPQ8RGwHveiV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***5. Hypothesis Testing***"
      ],
      "metadata": {
        "id": "g-ATYxFrGrvw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Based on your chart experiments, define three hypothetical statements from the dataset. In the next three questions, perform hypothesis testing to obtain final conclusion about the statements through your code and statistical testing."
      ],
      "metadata": {
        "id": "Yfr_Vlr8HBkt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "-7MS06SUHkB-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hypothetical Statement - 1"
      ],
      "metadata": {
        "id": "8yEUt7NnHlrM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. State Your research hypothesis as a null hypothesis and alternate hypothesis."
      ],
      "metadata": {
        "id": "tEA2Xm5dHt1r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "HI9ZP0laH0D-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Perform an appropriate statistical test."
      ],
      "metadata": {
        "id": "I79__PHVH19G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform Statistical Test to obtain P-Value"
      ],
      "metadata": {
        "id": "oZrfquKtyian"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which statistical test have you done to obtain P-Value?"
      ],
      "metadata": {
        "id": "Ou-I18pAyIpj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "s2U0kk00ygSB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Why did you choose the specific statistical test?"
      ],
      "metadata": {
        "id": "fF3858GYyt-u"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "HO4K0gP5y3B4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hypothetical Statement - 2"
      ],
      "metadata": {
        "id": "4_0_7-oCpUZd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. State Your research hypothesis as a null hypothesis and alternate hypothesis."
      ],
      "metadata": {
        "id": "hwyV_J3ipUZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "FnpLGJ-4pUZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Perform an appropriate statistical test."
      ],
      "metadata": {
        "id": "3yB-zSqbpUZe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform Statistical Test to obtain P-Value"
      ],
      "metadata": {
        "id": "sWxdNTXNpUZe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which statistical test have you done to obtain P-Value?"
      ],
      "metadata": {
        "id": "dEUvejAfpUZe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "oLDrPz7HpUZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Why did you choose the specific statistical test?"
      ],
      "metadata": {
        "id": "Fd15vwWVpUZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "4xOGYyiBpUZf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Hypothetical Statement - 3"
      ],
      "metadata": {
        "id": "bn_IUdTipZyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. State Your research hypothesis as a null hypothesis and alternate hypothesis."
      ],
      "metadata": {
        "id": "49K5P_iCpZyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "7gWI5rT9pZyH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Perform an appropriate statistical test."
      ],
      "metadata": {
        "id": "Nff-vKELpZyI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Perform Statistical Test to obtain P-Value"
      ],
      "metadata": {
        "id": "s6AnJQjtpZyI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which statistical test have you done to obtain P-Value?"
      ],
      "metadata": {
        "id": "kLW572S8pZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "ytWJ8v15pZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Why did you choose the specific statistical test?"
      ],
      "metadata": {
        "id": "dWbDXHzopZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "M99G98V6pZyI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***6. Feature Engineering & Data Pre-processing***"
      ],
      "metadata": {
        "id": "yLjJCtPM0KBk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Handling Missing Values"
      ],
      "metadata": {
        "id": "xiyOF9F70UgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling Missing Values & Missing Value Imputation"
      ],
      "metadata": {
        "id": "iRsAHk1K0fpS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### What all missing value imputation techniques have you used and why did you use those techniques?"
      ],
      "metadata": {
        "id": "7wuGOrhz0itI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "1ixusLtI0pqI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Handling Outliers"
      ],
      "metadata": {
        "id": "id1riN9m0vUs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling Outliers & Outlier treatments"
      ],
      "metadata": {
        "id": "M6w2CzZf04JK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What all outlier treatment techniques have you used and why did you use those techniques?"
      ],
      "metadata": {
        "id": "578E2V7j08f6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "uGZz5OrT1HH-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Categorical Encoding"
      ],
      "metadata": {
        "id": "89xtkJwZ18nB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode your categorical columns"
      ],
      "metadata": {
        "id": "21JmIYMG2hEo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### What all categorical encoding techniques have you used & why did you use those techniques?"
      ],
      "metadata": {
        "id": "67NQN5KX2AMe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "UDaue5h32n_G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Textual Data Preprocessing\n",
        "(It's mandatory for textual dataset i.e., NLP, Sentiment Analysis, Text Clustering etc.)"
      ],
      "metadata": {
        "id": "Iwf50b-R2tYG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Expand Contraction"
      ],
      "metadata": {
        "id": "GMQiZwjn3iu7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Expand Contraction"
      ],
      "metadata": {
        "id": "PTouz10C3oNN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Lower Casing"
      ],
      "metadata": {
        "id": "WVIkgGqN3qsr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lower Casing"
      ],
      "metadata": {
        "id": "88JnJ1jN3w7j"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Removing Punctuations"
      ],
      "metadata": {
        "id": "XkPnILGE3zoT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Punctuations"
      ],
      "metadata": {
        "id": "vqbBqNaA33c0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 4. Removing URLs & Removing words and digits contain digits."
      ],
      "metadata": {
        "id": "Hlsf0x5436Go"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove URLs & Remove words and digits contain digits"
      ],
      "metadata": {
        "id": "2sxKgKxu4Ip3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 5. Removing Stopwords & Removing White spaces"
      ],
      "metadata": {
        "id": "mT9DMSJo4nBL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Stopwords"
      ],
      "metadata": {
        "id": "T2LSJh154s8W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove White spaces"
      ],
      "metadata": {
        "id": "EgLJGffy4vm0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 6. Rephrase Text"
      ],
      "metadata": {
        "id": "c49ITxTc407N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Rephrase Text"
      ],
      "metadata": {
        "id": "foqY80Qu48N2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 7. Tokenization"
      ],
      "metadata": {
        "id": "OeJFEK0N496M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenization"
      ],
      "metadata": {
        "id": "ijx1rUOS5CUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 8. Text Normalization"
      ],
      "metadata": {
        "id": "9ExmJH0g5HBk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Normalizing Text (i.e., Stemming, Lemmatization etc.)"
      ],
      "metadata": {
        "id": "AIJ1a-Zc5PY8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which text normalization technique have you used and why?"
      ],
      "metadata": {
        "id": "cJNqERVU536h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "Z9jKVxE06BC1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 9. Part of speech tagging"
      ],
      "metadata": {
        "id": "k5UmGsbsOxih"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# POS Taging"
      ],
      "metadata": {
        "id": "btT3ZJBAO6Ik"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 10. Text Vectorization"
      ],
      "metadata": {
        "id": "T0VqWOYE6DLQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Vectorizing Text"
      ],
      "metadata": {
        "id": "yBRtdhth6JDE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which text vectorization technique have you used and why?"
      ],
      "metadata": {
        "id": "qBMux9mC6MCf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "su2EnbCh6UKQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. Feature Manipulation & Selection"
      ],
      "metadata": {
        "id": "-oLEiFgy-5Pf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Feature Manipulation"
      ],
      "metadata": {
        "id": "C74aWNz2AliB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Manipulate Features to minimize feature correlation and create new features"
      ],
      "metadata": {
        "id": "h1qC4yhBApWC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Feature Selection"
      ],
      "metadata": {
        "id": "2DejudWSA-a0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select your features wisely to avoid overfitting"
      ],
      "metadata": {
        "id": "YLhe8UmaBCEE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What all feature selection methods have you used  and why?"
      ],
      "metadata": {
        "id": "pEMng2IbBLp7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "rb2Lh6Z8BgGs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which all features you found important and why?"
      ],
      "metadata": {
        "id": "rAdphbQ9Bhjc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "fGgaEstsBnaf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. Data Transformation"
      ],
      "metadata": {
        "id": "TNVZ9zx19K6k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Do you think that your data needs to be transformed? If yes, which transformation have you used. Explain Why?"
      ],
      "metadata": {
        "id": "nqoHp30x9hH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Transform Your data"
      ],
      "metadata": {
        "id": "I6quWQ1T9rtH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. Data Scaling"
      ],
      "metadata": {
        "id": "rMDnDkt2B6du"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaling your data"
      ],
      "metadata": {
        "id": "dL9LWpySC6x_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which method have you used to scale you data and why?"
      ],
      "metadata": {
        "id": "yiiVWRdJDDil"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. Dimesionality Reduction"
      ],
      "metadata": {
        "id": "1UUpS68QDMuG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Do you think that dimensionality reduction is needed? Explain Why?"
      ],
      "metadata": {
        "id": "kexQrXU-DjzY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "GGRlBsSGDtTQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# DImensionality Reduction (If needed)"
      ],
      "metadata": {
        "id": "kQfvxBBHDvCa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which dimensionality reduction technique have you used and why? (If dimensionality reduction done on dataset.)"
      ],
      "metadata": {
        "id": "T5CmagL3EC8N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "ZKr75IDuEM7t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8. Data Splitting"
      ],
      "metadata": {
        "id": "BhH2vgX9EjGr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Split your data to train and test. Choose Splitting ratio wisely."
      ],
      "metadata": {
        "id": "0CTyd2UwEyNM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What data splitting ratio have you used and why?"
      ],
      "metadata": {
        "id": "qjKvONjwE8ra"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "Y2lJ8cobFDb_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9. Handling Imbalanced Dataset"
      ],
      "metadata": {
        "id": "P1XJ9OREExlT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Do you think the dataset is imbalanced? Explain Why."
      ],
      "metadata": {
        "id": "VFOzZv6IFROw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "GeKDIv7pFgcC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling Imbalanced Dataset (If needed)"
      ],
      "metadata": {
        "id": "nQsRhhZLFiDs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### What technique did you use to handle the imbalance dataset and why? (If needed to be balanced)"
      ],
      "metadata": {
        "id": "TIqpNgepFxVj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "qbet1HwdGDTz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***7. ML Model Implementation***"
      ],
      "metadata": {
        "id": "VfCC591jGiD4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Model - 1"
      ],
      "metadata": {
        "id": "OB4l2ZhMeS1U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 1 Implementation\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "7ebyywQieS1U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Explain the ML Model used and it's performance using Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "ArJBuiUVfxKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing evaluation Metric Score chart"
      ],
      "metadata": {
        "id": "rqD5ZohzfxKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Cross- Validation & Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "4qY1EAkEfxKe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 1 Implementation with hyperparameter optimization techniques (i.e., GridSearch CV, RandomSearch CV, Bayesian Optimization etc.)\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "Dy61ujd6fxKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which hyperparameter optimization technique have you used and why?"
      ],
      "metadata": {
        "id": "PiV4Ypx8fxKe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "negyGRa7fxKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "TfvqoZmBfxKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "OaLui8CcfxKf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Model - 2"
      ],
      "metadata": {
        "id": "dJ2tPlVmpsJ0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Explain the ML Model used and it's performance using Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "JWYfwnehpsJ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing evaluation Metric Score chart"
      ],
      "metadata": {
        "id": "yEl-hgQWpsJ1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Cross- Validation & Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "-jK_YjpMpsJ2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 1 Implementation with hyperparameter optimization techniques (i.e., GridSearch CV, RandomSearch CV, Bayesian Optimization etc.)\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "Dn0EOfS6psJ2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which hyperparameter optimization technique have you used and why?"
      ],
      "metadata": {
        "id": "HAih1iBOpsJ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "9kBgjYcdpsJ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "zVGeBEFhpsJ2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "74yRdG6UpsJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 3. Explain each evaluation metric's indication towards business and the business impact pf the ML model used."
      ],
      "metadata": {
        "id": "bmKjuQ-FpsJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "BDKtOrBQpsJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ML Model - 3"
      ],
      "metadata": {
        "id": "Fze-IPXLpx6K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 3 Implementation\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "FFrSXAtrpx6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 1. Explain the ML Model used and it's performance using Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "7AN1z2sKpx6M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualizing evaluation Metric Score chart"
      ],
      "metadata": {
        "id": "xIY4lxxGpx6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2. Cross- Validation & Hyperparameter Tuning"
      ],
      "metadata": {
        "id": "9PIHJqyupx6M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# ML Model - 3 Implementation with hyperparameter optimization techniques (i.e., GridSearch CV, RandomSearch CV, Bayesian Optimization etc.)\n",
        "\n",
        "# Fit the Algorithm\n",
        "\n",
        "# Predict on the model"
      ],
      "metadata": {
        "id": "eSVXuaSKpx6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Which hyperparameter optimization technique have you used and why?"
      ],
      "metadata": {
        "id": "_-qAgymDpx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "lQMffxkwpx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Have you seen any improvement? Note down the improvement with updates Evaluation metric Score Chart."
      ],
      "metadata": {
        "id": "Z-hykwinpx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "MzVzZC6opx6N"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Which Evaluation metrics did you consider for a positive business impact and why?"
      ],
      "metadata": {
        "id": "h_CCil-SKHpo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "jHVz9hHDKFms"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Which ML model did you choose from the above created models as your final prediction model and why?"
      ],
      "metadata": {
        "id": "cBFFvTBNJzUa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "6ksF5Q1LKTVm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. Explain the model which you have used and the feature importance using any model explainability tool?"
      ],
      "metadata": {
        "id": "HvGl1hHyA_VK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Answer Here."
      ],
      "metadata": {
        "id": "YnvVTiIxBL-C"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***8.*** ***Future Work (Optional)***"
      ],
      "metadata": {
        "id": "EyNgTHvd2WFk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. Save the best performing ml model in a pickle file or joblib file format for deployment process.\n"
      ],
      "metadata": {
        "id": "KH5McJBi2d8v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the File"
      ],
      "metadata": {
        "id": "bQIANRl32f4J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. Again Load the saved model file and try to predict unseen data for a sanity check.\n"
      ],
      "metadata": {
        "id": "iW_Lq9qf2h6X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the File and predict unseen data."
      ],
      "metadata": {
        "id": "oEXk9ydD2nVC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Congrats! Your model is successfully created and ready for deployment on a live server for a real user interaction !!!***"
      ],
      "metadata": {
        "id": "-Kee-DAl2viO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Conclusion**"
      ],
      "metadata": {
        "id": "gCX9965dhzqZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Write the conclusion here."
      ],
      "metadata": {
        "id": "Fjb1IsQkh3yE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### ***Hurrah! You have successfully completed your Machine Learning Capstone Project !!!***"
      ],
      "metadata": {
        "id": "gIfDvo9L0UH2"
      }
    }
  ]
}