The aim to decrease the maintenance cost of generators used in wind energy production machinery. This is achieved by building various classification models, accounting for class imbalance, and tuning on a user defined cost metric (function of true positives, false positives and false negatives predicted) & productionising the model using pipelines.

[College Course] - Course: BITS F312 Neural Network and Fuzzy Logic

Use Random Forest to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

ExcelR_Assignment---Decision-Tree---Assignment---14

Proyecto de machine learning que consiste en desarrollar un sistema de detección de transacciones fraudulentas.

The "Gold Price Prediction" project focuses on predicting the prices of gold using machine learning techniques. By leveraging popular Python libraries such as NumPy, Pandas, Scikit-learn (sklearn), Matplotlib, Seaborn, Random Forest Regressor, and others, this project provides a comprehensive solution for accurate price estimation.

KMeans Clustering of data using Sklearn library, numpy and Pickle data

In this problem i have tried to explain how XGB algorithm works in case of classification. I have also stated the accuracy score at the end for our XGBClassifier model. The confusion matrix has also been shown for the same. I have used the Kaggle Dataset - Titanic Survivors csv file.

Create a model that helps choose the region with the highest profit margin.

Case Study

Data Science - Naive Bayes Work

Data Science - Random Forest Work

ExcelR_Assignment---KNN---Assignment---13

Run three different classification algorithms for explaining whether region's economies grew by more than 5% based on the data provided. Standard goodness measures for classification algorithms also included.

📔 This repository delves into Logistic Regression for loan approval prediction at LoanTap. It covers data preprocessing, model development, evaluation metrics, and strategic business recommendations. Explore model optimization techniques such as confusion matrix, precision, recall, Roc curve and F1 score to effectively mitigate default risks.

Implementation of Support Vector Machine, and Random Forest Model using sklearn

Use decision trees to prepare a model on fraud data. Treating those who have taxable income <= 30000 as "Risky" and others are "Good" and A cloth manufacturing company is interested to know about the segment or attributes causes high sale.

Multi Linear Regression Assignment - 5

Use various techniques to train and evaluate a model based on loan risk.