https://github.com/mkhekare/mulytics_masters_union_casecomp

Analysis aims to address the challenges faced by a leading broadband provider in optimizing lead conversion processes
https://github.com/mkhekare/mulytics_masters_union_casecomp

case-competition case-study machine-learning-algorithms masters-union voting-classifier

Last synced: 3 months ago
JSON representation

Analysis aims to address the challenges faced by a leading broadband provider in optimizing lead conversion processes

• Host: GitHub
• URL: https://github.com/mkhekare/mulytics_masters_union_casecomp
• Owner: mkhekare
• Created: 2025-04-08T20:24:43.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-04-08T20:28:47.000Z (about 1 year ago)
• Last Synced: 2025-04-08T21:29:05.442Z (about 1 year ago)
• Topics: case-competition, case-study, machine-learning-algorithms, masters-union, voting-classifier
• Language: HTML
• Homepage:
• Size: 3.17 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Step-by-Step Analysis for Lead Installation Forecasting

## Objective

This analysis aims to address the challenges faced by a leading broadband provider in optimizing lead conversion processes. The goal is to:

- **Optimize the Lead Funnel:**

  - Segment customers to identify high-conversion leads.

  - Allocate marketing budgets efficiently.

  - Prioritize high-potential leads for follow-ups.

- **Develop a Predictive Model:**

  - Identify features impacting lead conversions.

  - Build models to predict lead installation success.

## Overview of Steps

### 1. Data Preprocessing

- Handled missing values in critical columns like `days_to_accept` and `days_to_install_request`.

- Renamed columns for better readability.

- Applied encoding (One-Hot and Label Encoding) for categorical variables.

- Balanced the target variable using SMOTE to handle class imbalance.

### 2. Exploratory Data Analysis (EDA)

- Visualized data distributions using histograms and countplots.

- Analyzed correlations between numerical variables and the target using heatmaps.

- Highlighted insights like key metrics (`marketing_spend_inr`, `days_to_qualify`) that influence conversions.

### 3. Predictive Modeling

- Built and compared multiple models:

  - **Random Forest** for robust feature importance.

  - **XGBoost** for handling complex patterns.

  - **Voting Classifier** to combine strengths of multiple models.

  

- Evaluated models using:

  - Classification Reports for precision, recall, and F1-score.

  - ROC-AUC Score to assess overall performance.

  - Confusion Matrices for understanding true/false positives and negatives.

### 4. Feature Importance and Recommendations

- Identified top features like `days_to_install_request` and `marketing_spend_inr`.

- Provided actionable strategies to prioritize resources and improve marketing efficiency.

## Tools and Techniques Used

- **Libraries:** `pandas`, `numpy`, `seaborn`, `matplotlib`, `scikit-learn`, `XGBoost`, `imblearn`.

  

### Preprocessing:

- Missing value imputation.

- Encoding categorical variables.

- Balancing classes with SMOTE.

### Visualization:

- Heatmaps, countplots, histograms, and barplots.

### Machine Learning Models:

- Random Forest, XGBoost, and Voting Classifier.

## Key Deliverables

- **Data Cleaning and EDA:**

  - Insights into lead behaviors and operational metrics.

  

- **Feature Importance Analysis:**

  - Identification of critical predictors for conversions.

  

- **Predictive Model Performance:**

  - Models evaluated on precision, recall, and ROC-AUC score.

  

- **Actionable Recommendations:**

  - Strategies for improving lead prioritization and marketing efficiency.