Data Analysis and Machine Learning with Python: EDA with ECDF and Correlation analysis, Preprocessing and Feature engineering, L1 (Lasso) Regression and Random Forest Regressor with scikit-learn backed up by cross-validation, grid search and plots of feature importance.

Launching a mobile company to rival giants like Apple & Samsung, we leverage sales data to discern price ranges for mobiles based on features via ML models like SVM, DT, & RF. Not aiming for exact prices, but a strategic price bracket.

Uses a random forest (RF) regression model to predict bitcoin prices 3 days ahead. Trained on data from 2010-2019.

Real-estate price predictive analysis using Regression, RF , XGBoost and MLP models coded in scikit-learn and TensorFlow.

clean & EDA & train RF model to predict diamonds price

This is still not done, but here i make a model using RF where we predict the price of a new car on flask

Credit Risk Classification – Built Random Forest model to predict loan risk deployed using Flask API with web form UI. Laptop Price Prediction – Developed regression model with data preprocessing and feature engineering; deployed via Streamlit. California Housing Prices – Trained regression models (Ridge, RF) with log-scaling and model evaluation

mobile price training with model SVM, KNN, DT, RF

Prediction Model: Automobile Price forecasting using RF & GB regressor

Hybrid Model (RF & LGBM) to predicting CA single residence housing price

RF regression on AI models' usage's influence on stock market prices

Supervised machine learning model comparison (CatBoost, RF, SVM) to predict used car prices

Project based on Zomato Stock Price Prediction Machine Learning model using LR,DT&RF Method

Production-ready flight price prediction using ensemble models (RF + XGBoost + LightGBM) with AWS ECS deployment

This project predicts coffee prices using Random Forest (RF) and Support Vector Machine (SVR) models based on gas prices, oil prices, and temperature data. The RF model performs better than SVR, showing lower prediction errors and a more accurate fit to the real values.

Predict mobile prices using ML: RF, Naive Bayes, KNN, SVM algorithms. Features analysis, data preprocessing, and model evaluation.

NATURAL GAS SPOT PRICE PREDICTION USING THE HYBRID RANDOM FOREST-SUPPORT VECTOR REGRESSION-GENETIC ALGORITHM (RF-SVR-GA) MODEL

A project comparing the performance of a LR model and a RF model for the task of house price prediction, using scikit-learn

rforms mobile price range prediction using classification models. It includes data loading, cleaning, preprocessing (scaling), visualization, training baseline models (LR, KNN, RF, GB, SVC), hyperparameter tuning for RF and GB, and comparing model performance to identify the best predictor.

A Streamlit app for stock market prediction using ML models. Visualize indicators, view data, predict prices. Models: SARIMAX, RF, XGBoost, ANN, CNN, RNN, LSTM, GRU.

A collection of Python projects in quantitative finance and financial modeling, including portfolio optimization, Bayesian methods, asset pricing, options pricing, ML models (RF, XGBoost, deep learning), sentiment analysis, dashboards, data tools, and a stock pitch demonstrating financial intuition

Amazon (AMZN) stock price prediction using technical indicators, multi-model comparison (SVR, XGBoost, RF), and a custom Linear Regression implementation from scratch.

Exploratory data analysis and feature engineering methods applied.House price prediction models were made using (LR) , (KNN) ,(CART), (RF), (GBM), (XGBoost), (LightGBM)

Machine Learning regression project to predict car prices. Includes comprehensive EDA, Feature Engineering, model comparison (Linear, RF, Boosting), and Hyperparameter Tuning for optimized performance.

CRISP-DM house price prediction for Bolton residential area (R programming language): data prep, model comparison(MLR/SVR/Tree/RF), and Random Forest deployment.

This study analyzes the factors affecting price duration using ML and survival models. Based on daily data for 150 products across 289 supermarkets in Montevideo (2021–2024), it trains supervised models (e.g., XGBoost, RF) and survival models (Cox, RSF) to understand price change dynamics.

Developed a gold price prediction model using macroeconomic indicators. Evaluated multiple models (LR, Ridge, RF, XGBoost) using MAE, RMSE, and R², revealing performance differences and the impact of model choice on financial data prediction.

This project combines LSTM and Random Forest (RF) models for stock price forecasting. LSTMs capture time-based patterns, while RF enhances prediction accuracy with its ensemble learning approach. Using historical stock data, the model aims to deliver precise, risk-aware forecasts for better investment decisions.

R code for simulating consumer choice, constructing ground truth pricing, training RF/NN/XGBoost models, and evaluating model performance using both expected revenue and prediction accuracy on training and testing data.

Supplementary codes and datasets for "Artificial Intelligence-Based Modelling of European Electricity Prices Using SHAP Values". Includes data processing and model training (CatBoost, DNN, RF, SVM) with Optuna, using ENTSO-E data.