Projects in Awesome Lists by sayande01
A curated list of projects in awesome lists by sayande01 .
https://github.com/sayande01/diabatest_prediction_machine_learning_webapp
create a user-friendly web app using Streamlit, predicting diabetes risk from health data. Machine learning models trained on medical features like glucose, blood pressure, BMI offer personalized predictions. We empower users to make informed health decisions, potentially preventing diabetes.
Last synced: 06 Apr 2025
https://github.com/sayande01/100_days_of_aiml
Join me on my 100 Days of Data Science, AI, and ML Challenge! Each day, I work on projects and exercises to improve my skills in Python, machine learning, data analysis, and AI. This repository tracks my progress and showcases the knowledge gained across these domains
artificial-intelligence data-science deep-learning machine-learning
Last synced: 06 Apr 2025
https://github.com/sayande01/rock_mine_detection_machine_learning_project
It is a machine-learning project designed to distinguish between rocks and mines in underwater environments using logistic regression. With logistic regression as our primary model, we aim to develop a reliable and efficient solution for accurately identifying submerged objects, contributing to improved navigation safety, environmental monitoring
logistic-regression machine-learning sklearn
Last synced: 06 Apr 2025
https://github.com/sayande01/amazon_review_sentiment_analysis_webapp
Render deployment
Last synced: 06 Apr 2025
https://github.com/sayande01/food_delivery_profitibility_analysis
This project analyzes the cost structure and profitability of a food delivery service using a dataset of 1,000 orders. It explores financial metrics like order values, delivery fees, commission, payment processing fees, and refunds, identifying cost drivers and providing strategic recommendations for optimization.
Last synced: 06 Apr 2025
https://github.com/sayande01/food_delivery_time_prediction
Predicting food Delivery time based on various features like preparation time, weather, traffic level , time of the day, distance etc. day. Comparing Linear regression and Random forest to evaluate performance metrics
Last synced: 06 Apr 2025
https://github.com/sayande01/iris_eda_classification
This project applies a Random Forest classifier to the Iris dataset to predict flower species based on four features: Sepal Length, Sepal Width, Petal Length, and Petal Width. It includes data exploration, visualization, model building, evaluation, and hyperparameter tuning for optimal performance.
exploratory-data-analysis multiclass-classification random-forest
Last synced: 06 Apr 2025
https://github.com/sayande01/customer_churn_dnn
A Python project to predict customer churn using a neural network. Includes data preprocessing, exploratory analysis, and a TensorFlow-based model for binary classification. Ideal for understanding churn behavior and building predictive solutions.
Last synced: 06 Apr 2025
https://github.com/sayande01/laptop_price_predictor_webapp
Laptop Price predictor Web App deployed on Render
Last synced: 06 Apr 2025
https://github.com/sayande01/render_heart_disease
Heart disease prediction ML model deployed on Render
Last synced: 06 Apr 2025
https://github.com/sayande01/phising_url_detection_randomforest
This project builds a robust binary classification system to detect phishing websites using URL features like SSL state, web traffic, and domain patterns. Achieving 97% accuracy, 96.8% precision, and 0.9953 ROC-AUC, it ensures reliable, efficient detection with interpretable insights.
binary-classification logistic-regression random-forest-classifier roc-analysis svc
Last synced: 06 Apr 2025
https://github.com/sayande01/auction_art_price_prediction
This project analyzes the art auction market to uncover factors driving high-value bids. Using data on critic ratings, costs, trends, and artwork traits (e.g., artist reputation, historical value, medium), it employs EDA, statistics, and ML to provide actionable insights for stakeholders.
predictive-modeling random-forest-regression
Last synced: 06 Apr 2025
https://github.com/sayande01/loan_status_prediction_machine_learning_project
Last synced: 06 Apr 2025
https://github.com/sayande01/cars_msrp_prediction_pca
This project conducts an advanced exploratory data analysis (EDA) on a car dataset with the objective of identifying natural groupings, selecting optimal features for predicting the Manufacturer Suggested Retail Price (MSRP), and leveraging Principal Component Analysis (PCA) for dimensionality reduction.
dimensionality-reduction feature-selection linear-regression pca
Last synced: 06 Apr 2025
https://github.com/sayande01/consumer_shopping_trend_analysis
This project analyzes consumer shopping patterns using transaction data, demographics, and product categories to identify high-value customer segments, popular products, and retention trends. Insights will help optimize inventory, enhance customer satisfaction, and boost revenue through data-driven strategies.
Last synced: 06 Apr 2025
https://github.com/sayande01/predicting-future-energy-consumption-using-xgboost
"Using XGBoost to predict future energy consumption (in MW) based on historical data. This project includes extensive feature engineering and identifies key factors driving consumption patterns through feature importance analysis, enabling precise, data-driven forecasting."
time-series-forecasting xgboost-regression
Last synced: 06 Apr 2025
https://github.com/sayande01/natural_language_processing
This repository contains Jupyter notebooks and Python scripts that cover foundational concepts and practical implementations of NLP preprocessing techniques. Each topic is accompanied by clear explanations and code examples using the Natural Language Toolkit (NLTK) library.
bag-of-words natural-language-processing nltk stemming word2vec
Last synced: 06 Apr 2025
https://github.com/sayande01/eda-revision
Welcome to my EDA Mastery repository! Here, I analyze diverse datasets to uncover insights, identify patterns, and visualize data. Each analysis tackles unique challenges, from distributions to correlations, strengthening my EDA skills and building a strong data exploration foundation.
exploratory-data-analysis visualization
Last synced: 06 Apr 2025
https://github.com/sayande01/amazon_review_sentiment_analysis
This project is a tutorial on sentiment analysis in Python, using two approaches: VADER and the transformer-based RoBERTa model from Hugging Face. The Jupyter Notebook guides you through data preprocessing, model implementation, and result interpretation, showcasing traditional and advanced NLP techniques for sentiment analysis.
nltk roberta-model transformer vader-sentiment-analysis
Last synced: 06 Apr 2025
https://github.com/sayande01/kaggle_notebooks
This repo features Kaggle notebooks with data science projects, including EDA, predictive analytics, and competition entries. It highlights practical applications of ML techniques and showcases continuous skill development in data science.
eda machine-learning predictive-analytics
Last synced: 06 Apr 2025
https://github.com/sayande01/fake_news_detection_logisticregression
This project detects fake news using Logistic Regression with NLP techniques, including NLTK stopword removal, Porter Stemmer for text normalization, and TF-IDF vectorization for feature extraction. It achieves high accuracy and precision, offering a reliable solution to combat misinformation.
logistic-regression nltk porter-stemmer stopwords tf-idf-vectorizer
Last synced: 06 Apr 2025
https://github.com/sayande01/deep_learning
This repository serves as a comprehensive guide to deep learning concepts, designed to evolve from fundamental ideas to advanced techniques. Starting with the basics of perceptrons and moving through the intricacies of multilayer perceptrons (MLPs), this repository aims to provide a structured learning path for anyone interested in Deep learning
Last synced: 06 Apr 2025
https://github.com/sayande01/deep_learning_ml
Developed a credit risk analysis project using Keras and TensorFlow. Implemented neural networks to assess and predict credit risk based on historical financial data. Features include model training, evaluation, and predictions to help financial institutions manage risk and make informed lending decisions.
Last synced: 06 Apr 2025
https://github.com/sayande01/retail_customer_segmentation_kmeans_clustering
Last synced: 06 Apr 2025
https://github.com/sayande01/stroke_prediction_decisontree_eda
Predicting stroke risk using machine learning based on health and lifestyle factors like age, gender, hypertension, heart disease, glucose level, BMI, and smoking status. The project includes data preprocessing, encoding, standardization, and applying models to accurately predict strokes and identify key risk factors.
Last synced: 06 Apr 2025
https://github.com/sayande01/non_linear_clustering_dbscan
Built a DBSCAN clustering to identify patterns in complex datasets. Utilized density-based spatial clustering to group data points based on proximity and noise, providing robust analysis and pattern detection. Includes detailed implementation and visualization to aid data exploration and insights.
Last synced: 06 Apr 2025
https://github.com/sayande01/customer_segmentation_using_k_means
Created a customer segmentation project using K-Means clustering to categorize customers into distinct groups based on purchasing behavior and demographics. Implemented data preprocessing, clustering, and visualization to uncover patterns and tailor marketing strategies for targeted customer engagement.
Last synced: 06 Apr 2025
https://github.com/sayande01/ensemble_learning_ml
This project investigates ensemble learning techniques, combining multiple models to enhance accuracy and robustness. It covers both basic methods (Max Voting, Averaging, Weighted Averaging) and advanced techniques (Stacking, Blending, Bagging, Boosting), aiming to improve predictive performance by addressing model weaknesses.
ensemble-learning voting-classifier voting-regressor
Last synced: 06 Apr 2025
https://github.com/sayande01/unsupervised_learning_ml
This project merges unsupervised learning with Association Rule Learning to analyze retail market basket data. By applying K-Means, DBSCAN, Apriori, Eclat, and FP-Growth algorithms, it uncovers purchasing patterns and segments customers into clusters, aiming to optimize product placement, promotions, and product development.
apriori-algorithm dbscan fp-growth-algorithm k-means-clustering
Last synced: 06 Apr 2025
https://github.com/sayande01/breast_cancer_prediction_decision_tree-classifier
Last synced: 06 Apr 2025
https://github.com/sayande01/adclick_prediction_logistic_regression
Developed an ad-click prediction model using Logistic Regression to forecast user interactions with online advertisements. Analyzed features such as user behavior and demographics to predict click-through rates. The project includes data preprocessing, model training, and evaluation to enhance ad targeting and marketing strategies.
Last synced: 06 Apr 2025
https://github.com/sayande01/model_evaluation
This notebook explores key metrics for evaluating machine learning models: Accuracy, Precision, Recall, True Positive Ratio (TPR), False Positive Ratio (FPR), and the ROC Curve. It offers detailed explanations, calculations, and visualizations, demonstrating their roles in assessing classification model performance.
Last synced: 06 Apr 2025
https://github.com/sayande01/heart_disease_decison_tree
Developed a heart disease prediction model using Decision Tree algorithms. Analyzed patient data to classify risk levels and predict the likelihood of heart disease. Features include data preprocessing, model training, and performance evaluation, aimed at improving early diagnosis and personalized healthcare.
Last synced: 06 Apr 2025
https://github.com/sayande01/cancer_detection_support_vector_machine_classifier
Built a cancer detection model using Support Vector Machine (SVM) classifiers. Utilized SVM to analyze patient data and classify cancerous vs. non-cancerous cases with high accuracy. Includes data preprocessing, model training, and evaluation to support early detection and improve diagnostic outcomes.
Last synced: 06 Apr 2025
https://github.com/sayande01/gold_price_prediction_using_time_series_forecasting
Forecasting gold prices with machine learning, employing Linear Regression and Naive models. Analyzing historical data to predict future prices, aiding decision-making in financial markets.
linear-regression timeseries-forecasting
Last synced: 06 Apr 2025
https://github.com/sayande01/student_placement_prediction_webapp_render_deployed
This project uses machine learning algorithms (Random Forest Classifier and Decision Tree) to predict student placement likelihood based on age, gender, CGPA, internships, and backlogs. It provides actionable employability insights, aiding career planning. A user-friendly Flask web app will be deployed on Render for broad accessibility.
decsion-tree random-forest-classifier render-deployment
Last synced: 06 Apr 2025
https://github.com/sayande01/india_election_2019_data_analysis_ml_prediction
Exploring India's 2019 elections via data analysis to unveil trends in candidate profiles, voting patterns, and socio-political factors, aiming to offer insights into this historic event.
Last synced: 06 Apr 2025
https://github.com/sayande01/student_dropout_prediction_ml
Analyzing Gujarat's education system with ML, we predict student dropout rates using demographic, economic, academic, and social data. Rigorous preprocessing, feature engineering, and model training aim to develop accurate dropout prediction models. Insights gained inform targeted interventions for dropout prevention.
predictive-analytics rfc svc xgboost
Last synced: 06 Apr 2025
https://github.com/sayande01/customer_spending_data_analysis_python
In this project, we used Python to analyze customer spending patterns. By diving into a dataset containing transaction details like revenue, customer demographics, and product categories, we aimed to understand how different factors affect how much customers spend.
Last synced: 06 Apr 2025
https://github.com/sayande01/thyroid_disease_prediction_ml
This project develops an advanced predictive model to identify thyroid disease recurrence using machine learning algorithms. We used a detailed dataset with demographic, medical, and clinical features, and implemented Logistic Regression, Decision Tree, Random Forest, and CatBoost Classifier. Rigorous preprocessing and EDA were performed.
catboost-classifier machine-learning predictive-modeling
Last synced: 06 Apr 2025
https://github.com/sayande01/sales_prediction_linear_regression
Developed a sales prediction model using Linear Regression to forecast future sales based on historical data. Implemented data preprocessing, model training, and performance evaluation to provide accurate sales forecasts, aiding in inventory management and strategic planning.
Last synced: 06 Apr 2025
https://github.com/sayande01/glim_data_analytics_aimldl_statistics
In this repository i will be storing all the Jupyter notebook ipynb files and dataset files csv and excel that are used for Exploratory data analysis, statistical analysis, Machine learning etc in the Analytics class
Last synced: 06 Apr 2025
https://github.com/sayande01/heart_disease_prediction-random_forest
Implemented a heart disease prediction model using Random Forest. Leveraged ensemble learning to analyze patient data and predict heart disease risk with improved accuracy. The project includes data preprocessing, model training, and performance metrics to aid in early diagnosis and personalized treatment strategies.
Last synced: 06 Apr 2025
https://github.com/sayande01/california_housing_prediction_ml
Utilizing ML techniques, the project builds a predictive model for housing prices, leveraging diverse features like location, size, amenities, and neighborhood details. Using a rich dataset, it aims to deliver a precise and insightful tool for real estate professionals.
decision-tree linear-regression random-forest
Last synced: 06 Apr 2025
https://github.com/sayande01/face_recognition_opencv_machine_learning
Utilize OpenCV & face_recognition library to enable webcam-based face recognition. Encode and compare facial features in real-time for accurate identification. Tutorial covers environment setup, webcam video capture, face detection, encoding, comparison, and live recognition.
face-recognition opencv-python
Last synced: 06 Apr 2025
https://github.com/sayande01/crop_recommendation_system_machine_learning
The Smart Crop Recommendation System utilizes machine learning to suggest optimal crops based on soil and environmental factors like Nitrogen, Phosphorus, Potassium, Temperature, Humidity, pH, and Rainfall, aiding farmers in maximizing yield and sustainability.
Last synced: 06 Apr 2025
https://github.com/sayande01/aids_infection_prediction_machine_learning
This project uses machine learning to predict AIDS virus infection with 95% accuracy. By applying logistic regression and random forest algorithms, it involves data preprocessing, feature selection, model training, and evaluation. Comparing these models will identify the most effective method, aiding in early detection and treatment strategies.
logistic-regression random-forest-classifier
Last synced: 06 Apr 2025
https://github.com/sayande01/global_sustainable_energy_data_analysis_python
Last synced: 06 Apr 2025
https://github.com/sayande01/handwritten_digits_recognition_nn
This project leverages TensorFlow and Keras to implement deep learning techniques, focusing on CNNs for recognizing handwritten digits from images. Integrated with OpenCV, it ensures precise digit extraction through robust image preprocessing and manipulation.
keras-tensorflow neural-network
Last synced: 06 Apr 2025
https://github.com/sayande01/fake_news_prediction_machine_learning
This project evaluates logistic regression, random forest, decision tree, and gradient boosting classifier models for fake news detection. Using labeled data, it analyzes accuracy, confusion matrices, and ROC curves to understand each model's effectiveness in discerning between real and fake news.
binary-classification decison-trees gradient-boosting-classifier logistic-regression random-forest
Last synced: 06 Apr 2025
https://github.com/sayande01/employee_churn_prediction_machine_learning
Leveraging ColumnTransformer, pipelines, standardization, and encoding, we'll preprocess data. Using Logistic Regression, Decision Trees, Random Forest, and XGBoost, we'll analyze factors like job satisfaction, promotion, and salary to predict churn. This helps companies improve satisfaction, reduce turnover, and enhance stability.
decison-trees logistic-regression random-forest xgboost-classifier
Last synced: 06 Apr 2025
https://github.com/sayande01/book_recommender_system_machine_learning
This project aims to build an advanced book recommendation system by integrating collaborative filtering, content-based filtering, and machine learning. It offers tailored suggestions based on user preferences and interactions, using EDA for insights and cosine similarity and SVD for precise recommendations.
collaborative-filtering cosine-similarity scikit-surprise svd
Last synced: 06 Apr 2025
https://github.com/sayande01/anime_2024__data_analysis_python
The Anime Insights project aims to delve into the world of anime through data analysis, leveraging Python libraries such as Pandas, Matplotlib, and Seaborn. Anime has become a global phenomenon, with a diverse range of genres, styles, and themes captivating audiences worldwide. This project seeks to uncover insights into characteristics of anime
Last synced: 06 Apr 2025
https://github.com/sayande01/weather_forecasting_ml
"We're using advanced machine learning to predict weather better. By looking at past weather data, we're making accurate forecasts for things like temperature, humidity, and wind speed. This helps different industries plan better for weather-related events."
autoregressive-models forecasting time-series-analysis
Last synced: 06 Apr 2025
https://github.com/sayande01/amazon_alexa_customer_sentiment_analysis_machine_learning
Last synced: 06 Apr 2025
https://github.com/sayande01/hr_analytics_data_analysis_with_ml_attritionprediction
This project employs logistic regression and advanced analytics to predict employee attrition, enhancing organizational productivity. Leveraging machine learning, it develops a robust model using features like age, job satisfaction, and work environment. Through EDA, feature engineering, and grid search model tuning, it optimizes performance metric
attrition-rate logistic-regression
Last synced: 06 Apr 2025
https://github.com/sayande01/deep_learning_breast_cancer_classification_with_neuralnetwork
Developing a robust deep learning model for breast cancer classification, leveraging ML techniques to differentiate malignant and benign tumors from tissue data. Using CNNs and diverse datasets, we aim to enhance medical diagnostics, aiding informed healthcare decisions and improving patient outcomes
Last synced: 06 Apr 2025
https://github.com/sayande01/credit_card_fraud_detection_machine_learning
Autoencoder, ModelCheckpoint
Last synced: 06 Apr 2025
https://github.com/sayande01/singapore_property_price_data_analysis_python
Singapore's property market using diverse data attributes. Uncover trends, patterns, and correlations through data analysis and visualization, offering insights for buyers, sellers, and stakeholders
Last synced: 06 Apr 2025
https://github.com/sayande01/breast_cancer_classification_machine_learning_project
Last synced: 06 Apr 2025
https://github.com/sayande01/walmart_sales_data_analysis_mysql
This project involves cleaning and analyzing Walmart sales data using MySQL. It includes tasks such as data cleaning, preprocessing, and analysis to derive insights into sales trends, customer behavior, and product performance.
Last synced: 06 Apr 2025
https://github.com/sayande01/supermarket_data_analysis_mysql
Column names have been adjusted from the raw data file version to avoid column naming error
Last synced: 06 Apr 2025
https://github.com/sayande01/customer_segmentation_k-means_custering_machine_learning_project
Employ K-means clustering on customer income and spending data to segment the market effectively. Gain insights for targeted marketing and personalized strategies, enhancing customer satisfaction and driving business growth.
k-means-clustering machine-learning
Last synced: 06 Apr 2025