Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

This project focuses on time series forecasting to predict store sales for Corporation Favorita, a large Ecuadorian-based grocery retailer. The goal is to build a model that accurately predicts the unit sales for thousands of items sold at different Favorita stores.

Project Description: complaint Analysis : "Analyzing Complaints based on Complaint Types and Regions. Cleaning and Preprocessing the Complaint Types Data. To calculate Resolution Time for each Complaints and checking which Agency resolve most complaints. To check City, Location and Month wise Complaint Types. Hypothesis testing (Shapiro-Wilk test,Kruskal-Wallis test ). Implementation of ML algorithm for classification of Complaint Types according to Resolution Time (KNN, Random Forest Classifier) & prediction( linear Regression). Time series analysis on Complaints and forecasting next 1week ,1 month, Complaints.(Decomposition, Stationarity, Moving Average ,Holt Winters Method, SARIMAX Model). Evaluation of the models.

This project predicts mood using a hybrid ML approach with NLP. It utilizes BERT for sentiment analysis, LSTM for time series, and regressor models for forecasting. Data is stored in MySQL, visualized in Tableau, and accessed via a Streamlit GUI. SHAP and LIME ensure model interpretability.

In this paper we are predicting and forecasting the COVID-19 outbreak in World on machine learning approach, the aim of the project is to provide data analysis of covid-19 (a pandemic started in December 2019) using different kind of ML models such as SVM, polynomial regression and Prophet. Using those three models we predicting conform, death and recover cases for the World and we take one country which is India. Through plotting of data, various cases have been studied like most affected countries due to this pandemic as well as most affected state in India. In this paper we are using Time series dataset provided by Johns Hopkins University. The model is predicting the number of confirmed, recover and death cases based on data available from 22nd January, 2020 till up to today. In this project, the predictions on various cases have been done and finally, the accuracy of the algorithm has been determined. Comparison graphs has also been plotted to analyses how much World and INDIA is getting affected/recover day by day.

ML Project forecasting the closing price of Amazon's stock for a given day leveraging its historical performance data. The model performes time-series forecasting with Rolling OLS Regression.

GREEN STEEL project - MATLAB - Simulink ML based digital twin of Blast Furnace with Predictive Analysis. Applied Machine Learning techniques- LSTM-based time-series forecasting and nonlinear regression using the Levenberg–Marquardt (LM) algorithm to predict CO₂ emissions in a hydrogen-injected blast furnace

Walmart Weekly Sales Forecasting Capstone Project uses historical data from 45 stores to predict weekly sales. Combining Time Series models (SARIMA, Holt-Winters, Prophet) and ML models (Linear Regression, Random Forest, XGBoost), it reveals trends, holiday effects, and economic impacts for better inventory planning.

This project predicts mood using a hybrid ML approach with NLP. It utilizes BERT for sentiment analysis, LSTM for time series, and regressor models for forecasting. Data is stored in MySQL, visualized in Tableau, and accessed via a Streamlit GUI. SHAP and LIME ensure model interpretability.

The COVID-19 has spread rapidly around the world and the current condition is becoming worse day by day. People are infected by this virus at different times of the year. Since it is a global disease, it affects the death rate strongly. Therefore, it needs to be controlled for not spreading rapidly also it is necessary to keep track of that time when this virus spread more, and the number of patients being affected to reduce the damage of this outbreak. But it is difficult to analyse and predict the growth of this disease at different times of the year because with this dataset our current system provides the computerized data in a collective way. So, we need a ML algorithm to map the disease and its progression to overcome this problem. ML are two categories: one is supervised and unsupervised machine learning. Supervised machine learning includes some models like regression model, classification model, times series forecasting model etc. where unsupervised machine learning includes clustering models. In our project we used linear regression and time series forecasting model where for different time, lab-tests, death case etc. as input we perform regression and classification to analyse data for a particular time and predict the number of confirmed cases in future days from this disease. Using this model, we can get early predictions of the status of coronavirus at different times of the year. And get an estimate of how many people are infected with the virus at that time.

This project focuses on time series forecasting to predict store sales for Corporation Favorita, a large Ecuadorian-based grocery retailer. The goal is to build a model that accurately predicts the unit sales for thousands of items sold at different Favorita stores.

This is an advanced ML project with EDA, Time Series Regression, Clustering, Market Basket Analysis and Demand Forecasting.

Internship projects completed during Codveda Data Science Internship (2025). Tasks include regression, classification, and time-series forecasting using Python and ML.

AI & ML internship portfolio featuring regression, classification, clustering, time series forecasting, and CNN-based computer vision projects using Scikit-learn & TensorFlow.

A collection of AI/ML internship projects covering regression, classification, recommendation systems, time series forecasting, deep learning, and chatbot development with practical implementations and deployment.

A collection of end-to-end Data Science projects demonstrating skills in EDA, Regression, Classification, Time Series Forecasting, and Sentiment Analysis using Python and ML libraries.

End-to-end time series ML project forecasting Google stock closing prices. Features lag-based trend analysis, moving averages, Linear Regression modeling, and clear performance metrics.

Sales and demand forecasting project (ML Track Task 01) using time-series feature engineering, regression modeling, evaluation metrics, and business-focused forecast visualizations for planning inventory, staffing, and promotions.

This repository showcases a collection of machine learning projects completed during a [duration]-internship program. The projects focus on various ML tasks, including regression, classification, and time series forecasting

Personal Budgeting Forecasting Model using Time-Series ML (Linear Regression, FFNN, LSTM) for household financial prediction. Includes full dataset engineering pipeline, trained models, and charts. NYU Capstone Project.

This repository contains projects completed as part of the EE 769 - Introduction to Machine Learning course at IIT Bombay. These projects cover various ML applications, including classification, regression, and time-series forecasting.

Data analytics & ML projects covering KMeans clustering, hierarchical clustering, sentiment analysis (NLP), logistic regression, and time series forecasting. Includes preprocessing, model evaluation (AUC, MSE, Silhouette), and visualizations using Python libraries.

Weather Forecasting ML Project using Machine Learning and Time Series methods for weather prediction. Pipeline includes data collection, cleaning, EDA, feature engineering, and models such as Linear Regression, Random Forest, XGBoost, and ARIMA, with insights on forecast accuracy and temporal patterns.

End-to-end ML project on the Olist Brazilian E-Commerce dataset featuring EDA, statistical testing (ANOVA, Chi-Square), customer churn prediction, payment value regression, sentiment analysis (NLP), time-series forecasting (ARIMA/SARIMA/Prophet), and a deployed Streamlit app.

A research-level time series forecasting project that decomposes sales data into trend, seasonality, and residuals, modelling each with the most suitable regression method (Ridge/Lasso for trend, Polynomial/Fourier for seasonality, XGBoost/LightGBM for residuals). Blends statistical and ML for robust, accurate sales forecasts.

Electricity Demand Analysis & Forecasting A Data Science project using EDA & ML for electricity demand prediction. 🔹 Key Features: ✅ Data Integration & Preprocessing (missing values, feature engineering). ✅ EDA & Outlier Detection (time series, IQR, Z-score). ✅ ML Modeling & Evaluation (Regression, MSE, RMSE, R²). 📊 Tech: Python, Pandas, NumPy

PowerPulse is an end‑to‑end ML project to forecast household electricity usage using the Individual Household Electric Power Consumption dataset, featuring structured EDA, time‑series feature engineering, and comparison of multiple regression models with clear evaluation metrics.

Welcome to the Restaurant Revenue Prediction project repository! We embark on the task of predicting the annual sales of 100,000 regional restaurant locations for TFI company. By leveraging ML we aim to provide TFI with insights to optimize their restaurant expansion strategy. Key Features Include Regression and Time Series Forecasting

This project forecasts hourly taxi orders at airports using ML on time-series data. After resampling and feature engineering (lags up to 168h, rolling means), CatBoost achieves best RMSE of 37.30, outperforming Linear Regression, Decision Tree, LightGBM. Enables optimal driver scheduling during peak hours.

This Python ML project predicts short-term stock prices. Using yfinance, it extracts historical market data (Open, High, Low, Volume) to train a Scikit-Learn Linear Regression model. It employs strict chronological time-series data splitting to forecast the next day's exact closing price without data leakage, visualising results via Matplotlib.