maheshvarade/USA_Consumer_Finances_Kmens_Clustering

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maheshvarade/Poland-Bankruptcy-Prediction

Poland Bankruptcy Prediction (2009) This project aims to predict whether a Polish company went bankrupt in 2009 based on its financial data. The dataset contains several features derived from companies' balance sheets, and the goal is to build models that can identify bankruptcy effectively — despite the challenge of high class imbalance.

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maheshvarade/Enron-Spam-Detection-using-NLP-ML

This project leverages the Enron email dataset to build a spam detection model using classical machine learning techniques. The model processes and classifies emails based on their subject lines and message bodies, with a final accuracy of 90–91% using Logistic Regression and Multinomial Naive Bayes classifiers.

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maheshvarade/Mobile-Price-Classification-using-ML-SVM-Logistic-Regression-

This project tackles the challenge faced by a new mobile company founded by Prabhat, who wants to compete with tech giants like Apple and Samsung. The goal is to predict the price range of a mobile phone based on its features — not the exact price, but whether it's low, medium, high, or very high cost.

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maheshvarade/K-Nearest-Neighbors-KNN-for-Regression

This project implements KNN Regression on the Auto MPG dataset to predict fuel efficiency (MPG) based on features like horsepower, weight, acceleration, and displacement. 🔹 Key Highlights: Exploratory Data Analysis (EDA): Data cleaning, handling missing values, and correlation heatm

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maheshvarade/Rice-Grain-Classification-using-MobileNetV2

Rice Grain Classification Using MobileNetV2 This project focuses on classifying five types of rice grains (Arborio, Basmati, Ipsala, Jasmine, and Karacadag) using MobileNetV2. The dataset consists of 75,000 images (15,000 per class).

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maheshvarade/Transfer-Learning-for-Grapevine-Leaves-Detection

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maheshvarade/K-means-clustering_on-_credit_card_data

This project analyzes Credit Card Customer Data to segment users based on their Annual Income and Spending Score using K-Means clustering. The goal is to identify distinct customer groups for better marketing strategies.

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maheshvarade/Diabetes-Prediction

Handling missing values and outliers improved data quality. Balancing the dataset using SMOTE helped improve model performance. Future improvements can include hyperparameter tuning and trying different models for better accuracy.

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maheshvarade/Tree-Based-Algorithms-on-loan-data

Loan Risk Prediction Implemented RandomForestClassifier, which provided the best results compared to Gradient Boosting.Car Ownership Prediction (Using Same Data) Compared multiple models.House Ownership Prediction Followed the same pipeline for predicting House Ownership.

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maheshvarade/decision-tree-for-classification-of-penguins-species

🐧 Decision Tree for Classification of Penguin Species This project implements a Decision Tree Classifier to classify different species of penguins using the Palmer Penguins dataset. The dataset contains features such as bill length, bill depth, flipper length, and body mass to predict the species (Adelie, Chinstrap, or Gentoo).

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maheshvarade/KNN

🚗 KNN Classification for Car Safety | Implements K-Nearest Neighbors (KNN) to classify car safety levels using the Car Evaluation Dataset. Features data preprocessing, feature scaling, and hyperparameter tuning with RandomizedSearchCV, resulting in improved accuracy. 📊🔍

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maheshvarade/ann-for-rain-in-USA-datasate-

In this project, I perform preprocessing by converting categorical features, such as dates, into numerical values using Label Encoding. I then conduct Exploratory Data Analysis (EDA) to understand the dataset. After splitting the data into training and testing sets, I scale the features to a range between 0 and 1 using Standard Scaling. Finally, I

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maheshvarade/NLP

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