Udacity Data Analyst Nanodegree - Project III

A trading algorithm that identifies stocks with the largest potential for growth while heavily considering its volatility using quantopian

A regression based modeling project to forecast the sales of Walmart

The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.

My notes in Jupyter Notebooks for statistics, probability, and plotting applied with major python libraries as an introduction to machine learning.

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.

Random Good Data Science Stuff

M.S. and EPGP Assignment - Linear Regression model for a US bike-sharing provider BoomBikes

An implementation of an ARIMA time series forecast using Python statsmodels and scipy.

Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?

Prediction of nitrogen dioxide concentration in air using linear regression.

Linear Regression Bike sharing Assignment

Time Series Analysis

in this repository i will upload all the projects which i would make regarding deep learing. i will upload as much neural networks as possible.

This project analyzes key socioeconomic and health indicators influencing life expectancy in developing countries, using regression models and statistical techniques to derive actionable insights from WHO and UN datasets.

Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing

Assignment-04-Simple-Linear-Regression-1 Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Mod…

I perform a retrospective analysis on the linear regression analysis that I previously performed on the NYC Bike Counts dataset. Specifically, I analyze my linear regression analysis to identify anything that I could have done differently.

Model to identify the potential lead by assigning a score for their rate of conversion. Therefore, reaching out to potential is no more a brainstorming task.