This project aims to build a logistic regression model to assign a lead score between 0 and 100 to each lead. The lead score can be utilized by the company to target potential leads effectively. A higher score indicates a hot lead, more likely to convert, while a lower score suggests a cold lead with a lower likelihood of conversion.

The project involves data cleaning, exploratory data analysis (EDA), and feature engineering to prepare the dataset for training the logistic regression model. Logistic regression is employed to build the lead scoring model. The model is trained on historical data with features related to leads and their conversion outcomes. The trained model assigns a lead score between 0 and 100 based on the input features. This score can be used by the company to prioritize leads and optimize conversion efforts.

• Lead_Scoring_Project.ipynb: Jupyter Notebook containing code for data cleaning, exploratory data analysis, and feature engineering. The logistic regression model training code.

• Requirements.txt: File listing the Python packages and their versions required for the project.

Himanshu Thakur

This project is licensed under the MIT License.