GitHub / SridharYadav07 1 Repository
SridharYadav07/personalized-healthcare-medicine-recommendation
Personalized healthcare medicine recommendation system using Machine Learning and FastAPI
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SridharYadav07/Code_Alpha-_Task-4
This project demonstrates the typical steps in a machine learning pipeline from data preprocessing and cleaning to training a model and evaluation its performance. The use of Random Forest is appropriate here given the complexity of the dataset, and with further tuning and improvements, this model could be used to make accurate predictions on heart
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SridharYadav07/Code_Alpha_Task-3_Handwritten-Character-Recognition
This project is a handwritten letter recognition system that uses a convolutional neural network to classify 28*28 grayscale images of uppercase letters. The model is trained using a dataset of handwritten letters, and after training, it predicts the corresponding letter for a given input image. The project leverages libraries such as keras, opencv
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SridharYadav07/Code_Alpha_Creditcard_Scoring
This repository serves as a comprehensive example of how to preprocess data, train a Random Forest classifier, and evaluate its performance using several key metrics. The insights gained from the confusion matrix, classification report, and ROC-AUC score help to assess where the model excels and where improvements might be needed.
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SridharYadav07/Data-Science-Project-British-Airways-Reviews-
This project showcases skills in web scraping, data preprocessing, sentiment analysis, machine learning, and data visualization, making it a comprehensive example of applying data science techniques to real-world problems.
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SridharYadav07/Machine-Learning-Project-Bankruptcy-Prevention-
The project explores multiple machine learning algorithms and evaluates their performance using various metrics, such as accuracy and confusion matrices. The models tested include Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM). In addition, regularization techniques (L1, L2) are used to avoid overfit.
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SridharYadav07/Machine-Learning-Project-Combined-cycle-Power-Plant-
This project is focused on Multiple machine learning models, including Linear Regression, Decision Tree Regression, and Random Forest Regression, were implemented to predict the target variable and evaluated using various metrics like RMSE, MAE, and R-squared. The performance of these models was compared, and the Random Forest Regressor was found.
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SridharYadav07/E-Commerce-Transactions
This repository contains an end-to-end solution for analyzing an eCommerce Transactions dataset. It includes Exploratory Data Analysis(EDA), a Lookalike Model for customer recommendations, and customer segmentation using clustering techniques. The repository demonstrates insights into customer behavior, product sales, and make actionable decisions.
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