{"id":34912530,"url":"https://github.com/mkhekare/mulytics_masters_union_casecomp","last_synced_at":"2026-03-14T23:41:53.514Z","repository":{"id":286887141,"uuid":"962883251","full_name":"mkhekare/mulytics_masters_union_casecomp","owner":"mkhekare","description":"Analysis aims to address the challenges faced by a leading broadband provider in optimizing lead conversion processes","archived":false,"fork":false,"pushed_at":"2025-04-08T20:28:47.000Z","size":3321,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T21:29:05.442Z","etag":null,"topics":["case-competition","case-study","machine-learning-algorithms","masters-union","voting-classifier"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mkhekare.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-04-08T20:24:43.000Z","updated_at":"2025-04-08T20:29:39.000Z","dependencies_parsed_at":"2025-04-08T21:39:41.482Z","dependency_job_id":null,"html_url":"https://github.com/mkhekare/mulytics_masters_union_casecomp","commit_stats":null,"previous_names":["mkhekare/mulytics_masters_union_casecomp"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mkhekare/mulytics_masters_union_casecomp","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkhekare%2Fmulytics_masters_union_casecomp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkhekare%2Fmulytics_masters_union_casecomp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkhekare%2Fmulytics_masters_union_casecomp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkhekare%2Fmulytics_masters_union_casecomp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mkhekare","download_url":"https://codeload.github.com/mkhekare/mulytics_masters_union_casecomp/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mkhekare%2Fmulytics_masters_union_casecomp/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30521608,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-14T19:51:21.629Z","status":"ssl_error","status_checked_at":"2026-03-14T19:51:12.959Z","response_time":57,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["case-competition","case-study","machine-learning-algorithms","masters-union","voting-classifier"],"created_at":"2025-12-26T11:39:02.187Z","updated_at":"2026-03-14T23:41:53.509Z","avatar_url":"https://github.com/mkhekare.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Step-by-Step Analysis for Lead Installation Forecasting\n\n## Objective\nThis analysis aims to address the challenges faced by a leading broadband provider in optimizing lead conversion processes. The goal is to:\n\n- **Optimize the Lead Funnel:**\n  - Segment customers to identify high-conversion leads.\n  - Allocate marketing budgets efficiently.\n  - Prioritize high-potential leads for follow-ups.\n\n- **Develop a Predictive Model:**\n  - Identify features impacting lead conversions.\n  - Build models to predict lead installation success.\n\n## Overview of Steps\n\n### 1. Data Preprocessing\n- Handled missing values in critical columns like `days_to_accept` and `days_to_install_request`.\n- Renamed columns for better readability.\n- Applied encoding (One-Hot and Label Encoding) for categorical variables.\n- Balanced the target variable using SMOTE to handle class imbalance.\n\n### 2. Exploratory Data Analysis (EDA)\n- Visualized data distributions using histograms and countplots.\n- Analyzed correlations between numerical variables and the target using heatmaps.\n- Highlighted insights like key metrics (`marketing_spend_inr`, `days_to_qualify`) that influence conversions.\n\n### 3. Predictive Modeling\n- Built and compared multiple models:\n  - **Random Forest** for robust feature importance.\n  - **XGBoost** for handling complex patterns.\n  - **Voting Classifier** to combine strengths of multiple models.\n  \n- Evaluated models using:\n  - Classification Reports for precision, recall, and F1-score.\n  - ROC-AUC Score to assess overall performance.\n  - Confusion Matrices for understanding true/false positives and negatives.\n\n### 4. Feature Importance and Recommendations\n- Identified top features like `days_to_install_request` and `marketing_spend_inr`.\n- Provided actionable strategies to prioritize resources and improve marketing efficiency.\n\n## Tools and Techniques Used\n- **Libraries:** `pandas`, `numpy`, `seaborn`, `matplotlib`, `scikit-learn`, `XGBoost`, `imblearn`.\n  \n### Preprocessing:\n- Missing value imputation.\n- Encoding categorical variables.\n- Balancing classes with SMOTE.\n\n### Visualization:\n- Heatmaps, countplots, histograms, and barplots.\n\n### Machine Learning Models:\n- Random Forest, XGBoost, and Voting Classifier.\n\n## Key Deliverables\n- **Data Cleaning and EDA:**\n  - Insights into lead behaviors and operational metrics.\n  \n- **Feature Importance Analysis:**\n  - Identification of critical predictors for conversions.\n  \n- **Predictive Model Performance:**\n  - Models evaluated on precision, recall, and ROC-AUC score.\n  \n- **Actionable Recommendations:**\n  - Strategies for improving lead prioritization and marketing efficiency.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkhekare%2Fmulytics_masters_union_casecomp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmkhekare%2Fmulytics_masters_union_casecomp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkhekare%2Fmulytics_masters_union_casecomp/lists"}