flowchart TD
    subgraph "Data Sources"
        DS1["Leads.csv"]:::data
        DS2["Leads Data Dictionary.xlsx"]:::data
    end

    DS1 -->|"feeds"| P1["Data Cleaning/Preprocessing"]:::process
    DS2 -->|"feeds"| P1

    P1 -->|"passes clean data to"| P2["Exploratory Data Analysis"]:::process

    P2 -->|"sends insights to"| P3["Model Building & Evaluation"]:::process

    subgraph "Output & Reporting"
        R1["Lead Scoring Case Study.ipynb"]:::report
        R2["Assignment Subjective Questions.docx"]:::report
        R3["Executive Summary of Lead Scoring Case Study.pdf"]:::report
        R4["Presentation.pdf"]:::report
        R5["README.md"]:::report
    end

    P3 -->|"generates results for"| R1

    PL["Python Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Statsmodels"]:::lib
    PL ---|"supports"| P2
    PL ---|"supports"| P3

    click DS1 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Leads.csv"
    click DS2 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Leads%20Data%20Dictionary.xlsx"
    click P1 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Lead%20Scoring%20Case%20Study.ipynb"
    click P2 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Lead%20Scoring%20Case%20Study.ipynb"
    click P3 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Lead%20Scoring%20Case%20Study.ipynb"
    click R1 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Lead%20Scoring%20Case%20Study.ipynb"
    click R2 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Assignment%20Subjective%20Questions.docx"
    click R3 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Executive%20Summary%20of%20Lead%20Scoring%20Case%20Study.pdf"
    click R4 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/Presentation.pdf"
    click R5 "https://github.com/geethasagarb/lead_scoring_case_study/blob/master/README.md"

    classDef data fill:#f9c,stroke:#333,stroke-width:2px,color:#000;
    classDef process fill:#bbf,stroke:#333,stroke-width:2px,font-weight:bold;
    classDef report fill:#cfc,stroke:#333,stroke-width:2px,color:#000;
    classDef lib fill:#ffc,stroke:#333,stroke-width:2px,color:#000;

This repository contains a case study focused on lead scoring using machine learning techniques. The goal is to predict the likelihood of leads converting into customers based on historical data.

Businesses often receive numerous leads, but not all of them convert into customers. Efficiently categorizing these leads based on their likelihood to convert helps in prioritizing follow-ups and optimizing marketing efforts. This project aims to develop a predictive model that scores leads to identify the most promising ones.

The dataset Leads.csv contains various attributes of leads such as:

• Prospect ID
• Lead Number
• Lead Origin
• Lead Source
• Do Not Email
• Do Not Call
• Converted
• Total Visits
• Total Time Spent on Website
• Page Views Per Visit
• Last Activity
• Country
• Specialization
• Current Occupation
• Tags, etc.

• Pandas: Data manipulation
• NumPy: Numerical computations
• Matplotlib, Seaborn: Data visualization
• Scikit-learn: Machine learning
• Statsmodels: Statistical modeling

• Load the dataset and inspect basic statistics.
• Check for duplicate values in key columns (Prospect ID and Lead Number).
• Display the first few rows and overall shape of the dataset.
• Generate descriptive statistics and data types information.

• Replace 'Select' values with NaN to handle unanswered questions.
• Drop columns with more than 40% missing values to ensure data quality.
• Handle remaining missing values appropriately.

1. Data Splitting: Split the data into training and testing sets.
2. Feature Scaling: Standardize numerical features.
3. Model Selection: Use algorithms like Logistic Regression.
4. Evaluation: Assess models using metrics like Confusion Matrix, Precision, Recall, and F1 Score.

• Accuracy: 80.57%
• Sensitivity: 79.72%
• Specificity: 81.08%

• Accuracy: 80.34%
• Sensitivity: 79.27%
• Specificity: 81.04%

The model demonstrates strong performance in predicting lead conversions with accuracy, sensitivity, and specificity above 80% for both training and test datasets. Key factors influencing higher conversion rates include:

• Using the 'Lead Add Form'
• Being a 'Working Professional'
• Spending more time on the website
• Referral leads from existing customers
• Channels like Google and Direct Traffic
• Engagement through 'SMS Sent' or 'Email Opened'

The most common specialization categories are 'Others', followed by Finance Management, HR Management, and Marketing Management.

1. Clone the Repository:

   git clone https://github.com/geethasagarb/lead_scoring_case_study.git
   cd lead_scoring_case_study

2. Install Required Libraries: Make sure you have the required libraries installed. You can install them using:

   pip install -r requirements.txt

3. Run the Jupyter Notebook: Open the Jupyter Notebook to explore and run the analysis:

   jupyter notebook Lead\ Scoring\ Case\ Study.ipynb

• Scikit-learn Documentation
• Pandas Documentation
• Matplotlib Documentation
• Seaborn Documentation
• Statsmodels Documentation