random-forest-regressor

Learning to create Machine Learning Algorithms

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure (POSCAR or CIF).

A Machine Learning Project implemented from scratch which involves web scraping, data engineering, exploratory data analysis and machine learning to predict housing prices in New York Tri-State Area.

I have used Multinomial Naive Bayes, Random Trees Embedding, Random Forest Regressor, Random Forest Classifier, Multinomial Logistic Regression, Linear Support Vector Classifier, Linear Regression, Extra Tree Regressor, Extra Tree Classifier, Decision Tree Classifier, Binary Logistic Regression and calculated accuracy score, confusion matrix and ROC(Receiver Operating Characteristic) and AUC(Area Under Curve) and finally shown how they are classifying the tweet in positive and negative.

This project uses a machine learning approach in order to predict the number of goals scored by two teams in a match and then calculates the winning team

In this project using New York dataset we will predict the fare price of next trip. The dataset can be downloaded from https://www.kaggle.com/kentonnlp/2014-new-york-city-taxi-trips The dataset contains 2 Crore records and 8 features along with GPS coordinates of pickup and dropoff

Determining the housing prices of California properties for new sellers and also for buyers to estimate the profitability of the deal.

Predict sales prices and practice feature engineering, RFs, and gradient boosting

A Machine Learning Case Study based on helping the company target customers by predicting the customer loyalty score based on the transactions data.

A Machine Learning Model that predicts price of pre-owned cars using Linear Regression and Random Forrest Regressor.

In this project, I use the Random forest algorithm to build the house price prediction model on a dataset with 16 features and 4600 samples from Kaggle. Random Forest Regressor will be an optimal algorithm in this problem because it works well on both categorical and numerical features. Moreover, it is robust to missing values, new entries, and outliers and will save us the effort to normalize the data considering each feature’s scale varies a lot. The experiment shows that feature engineering using Recursive Feature Elimination does help improve model performance. Comparing the model trained on all features, the model trained on the 7 optimal features improves the R2 score from 0.4904 to 0.5435 while reducing the Mean absolute error from 134490.3 to 131453.1.

I participated in a Kaggle competition where I used a random forest regressor and hyper-parameter tuned my model to most accurately predict the sale price of a home based on multiple characteristics and features.

Hackathon by 'www.machinehack.com'. My Hackathon Rank : 228. Participants in Hackathon : 2600+ (till date 12/01/2021). ML Algorithm used: Random Forest Regressor with hyperparameter tuning.

My first Machine Learning Project of predicting the housing prices of a particular Housing Real Estate by training and testing data on a given data set. The housing data set is imported from UCI Machine Learning Repository

High-precision indoor positioning framework for most wifi-enabled devices.

Repository for my ENSF 611 (machine learning) final project.

Density Estimation, HMM Signature Verification, K-Means and Gap Statistics, Random Forests

Data science practice project from "Hands-On Machine Learning with SciKit-Learn and TensorFlow"

This repository consist of various machine learning models along with the dataset. The models are trained with widely used ML algorithms like Gradient Boost , Random Forest etc. Pickle is used to serialize ML algorithms for predictions or availing it for the server use.

Gold Prices Prediction, I made this Machine Learning using Random Forest Regression model. Use this code Wisely :D

Studying Activision Blizzard's credit risk and forecasting financials

Using Wikipedia to predict US Senate races

Built ml models of Logistic Regressor and Random Forest Regressor for predicting total number of Corona cases in India for a given new date. Computed accuracies and applied prediction on a new date ordinal with univariate and bivariate analysis including data cleaning and preparation.

I have used random forest regressor for this project.

Prediction is done by Random Forest Regressor with the help of Hyperparameter Tuning for better accuracy.

The goal is to predict how likely individuals are to receive their H1N1 and seasonal flu vaccines. Specifically, you'll be predicting two probabilities: one for h1n1_vaccine and one for seasonal_vaccine. Each row in the dataset represents one person who responded to the National 2009 H1N1 Flu Survey. For details please visit the link: https://www.drivendata.org/competitions/66/flu-shot-learning/page/211/

Data Analysis and Machine Learning

Predict the mileage per gallon (mpg) for cars