Data analysis, visualization and prediction to predict whether a patient has benign or malignant breast cancer based on properties of the cancer

In this project, we have worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.

GridSearchCV For Model optimization

Predict survival on the Titanic and get familiar with ML basics.

Predicting the presence of heart disease based on several health-related factors and Performing - i.) Data Cleaning ii.) Data Pre-Processing iii.) EDA iv.) Compare 5 different classification algorithms (Logistic Regression, Decision Tree, Random Forest, KNN and SVC)

This project demonstrates building a classification model for imbalanced data. Feature engineering, feature selection and extensive EDA. Comparing of logistic regression, random forest and ADA Boost models are done before finalizing the best model.

A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.

Clustering validation with ROC Curves

Developed Machine Learning Model to categorize customer with more than 91% accuracy and 88% roc_auc score. Best ML model is selected after evaluating all performance metric like Accuracy, Precision & recall

PD model using Logistic Regression in Python

The goal is to eliminate manual work in identifying faulty wafers. Opening and handling suspected wafers disrupts the entire process. False negatives result in wasted time, manpower, and costs.

Build a logistics regression learning model on the given dataset to determine whether the customer will churn or not.

Time Series Classification Part 2 Binary and Multiclass Classification. An interesting task in machine learning is classification of time series. In this problem, we will classify the activities of humans based on time series obtained by a Wireless Sensor Network.

Identify which customer is willing to possess the insurance policy, so we campaign efficiently.

Predict the Burned Area of Forest Fire with Neural Networks and Predicting Turbine Energy Yield (TEY) using Ambient Variables as Features.

Lead scoring case study

Junky Union is creating a system to sort movie reviews. They aim to train a model to detect negative reviews using the IMBD dataset with polarity labeling. The model must classify reviews as positive or negative with an F1 score of at least 0.85.

Develop and train image classification models using advanced deep learning techniques to identify diseases specific to apples.