Flipkart Project-Labmentix-Proj1-EDA/Regression/Classification-DataCleaning, Visualization,ML Models скачать в хорошем качестве

Flipkart Project-Labmentix-Proj1-EDA/Regression/Classification-DataCleaning, Visualization,ML Models

This project focused on building a machine learning solution to improve customer support operations by predicting key outcomes from historical interaction data. After understanding the business objective, the dataset was cleaned and preprocessed by handling missing values, converting numerical fields properly, and encoding categorical variables to make them suitable for modeling. Exploratory analysis helped in understanding feature distributions and their relevance to business performance. A structured train-test split with stratification ensured fair evaluation across categories. Multiple models were explored, and an XGBoost classifier was implemented due to its strong performance on structured data and ability to capture complex relationships. The model was trained to predict support ticket categories based on customer details, product information, agent attributes, handling time, and CSAT score. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess performance and interpret the model’s business impact. The final outcome demonstrates how machine learning can automate ticket classification, reduce manual effort, improve routing efficiency, and support faster resolution times, ultimately enhancing customer satisfaction and operational effectiveness.