Using scikit-learn RandomizedSearchCV and cross_val_score for ML Nested Cross Validation

Using various supervised learning estimators in Sci-Kit Learn to get the best prediction accuracy if possible for the pima indians dataset.

Built Random Forest classifier from scratch on top of Scikit Learn decision trees. Using Scikit Learn to create data cleaning pipelines, perform grid searches for hyper parameter tuning, and decision tree modeling

Model-Validation-Methods

Calculate the bias of k-fold cross-validation with hyper-parameter configuration

In this project, I have developed a Machine Learning model to predict whether users will click on ads. By analyzing various characteristics of users who click on ads, we can gain valuable insights and optimize ad campaigns for better engagement.

K Nearest Neighbours in Python

Exploring a music dataset by examining correlations between numerical variables, running a principal component analysis for dimensionality reduction and finally fitting both scikit learn Decision Tree Classification and Logistic Regression models to compare their performance.

This is a Kaggle Dataset where we classify the cars using their various features. Here I used plotly to visualize the Accuracy Scores. Also I used CrossValScore to get More accurate Accuracy Score.

Iris dataset

Machine learning model which can predict the strength of a mixture for given composition of ingredients like cement, slag, ash, water, superplastic, coarse aggregates, fine aggregates, age.

Titanic Survivor Analysis and Prediction

GridSearchCV, RandomSearchCV For Model optimization and Saving/Loading the model

pipelines chains together multiple steps so that the output of each step is used as input to the next step

GridSearchCV For Model optimization

Study Project for Yandex Practicum

A model which can predict if the customer will pay the loan or not.