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

I developed a sophisticated ML model using LLMs to predict user preferences in chatbot interactions.implemented a comprehensive data preprocessing pipeline,including feature extraction and encoding,to optimize performance. conducted extensive hyperparameter tuning and evaluation, enhancing accuracy and in AI-driven conversational systems.

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

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.

Model-Validation-Methods

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.

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

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

This project focuses on detecting and classifying brain tumors (NonDemented, Very Mild Demented, Mild Demented, Moderate Demented)

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

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

Different types of supervised learning models used for classification problem. Included cross validation for finding hyperparameters whenever necessaruy.