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Machine Learning Projects: This repo includes 3 projects using Logistic Regression, XGBoost, Random Forest, Linear Regression, Lasso, Ridge, and DBSCAN. It covers both supervised and unsupervised learning techniques with detailed code and data preprocessing.

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1) Conversion Rate Prediction Model

Company's Description 📇

Data Science Weekly is a famous newsletter curated by independent data scientists. Anyone can register his/her e-mail address on this website to receive weekly news about data science and its applications!

Project Overview 🚧

The objective of this project is to build a model that predicts if a given user will subscribe to the newsletter, using just a few pieces of information about the user. Additionally, the parameters of the model will be analyzed to highlight features that are important in explaining the behavior of users. The assessment of the models' performance will be based on the F1-score metric.

Data

  • data_train.csv: This file contains labeled data, including both explanatory variables (X) and the target variable to be predicted (Y).
  • data_test.csv: This file contains "new" examples that have not been used to train the model. It is in the same format as data_train.csv but is unlabeled.

2) Walmart Sales Prediction Model

Company's Description 📇

Walmart Inc. is an American multinational retail corporation that operates a chain of hypermarkets, discount department stores, and grocery stores from the United States, headquartered in Bentonville, Arkansas. The company was founded by Sam Walton in 1962.

Project Overview 🚧

The objective of this project is to build a machine learning model capable of estimating the weekly sales in Walmart stores with the best precision possible on the predictions made. By developing such a model, we aim to gain insights into how sales are influenced by various economic indicators and to use these insights for planning future marketing campaigns.

Data

The dataset used for this project includes historical sales data from Walmart stores, Store ,Date ,Weekly_Sales, Holiday_Flag, Temperature, Fuel_Price, CPI ,Unemployment. This dataset will be used for training and evaluating the machine learning models.

3) Uber Hot-Zone Identification Project

The code on GitHub may not be fully displayed. You can access the complete project by downloading it.

Also you can check it here : Uber Project

Company's Description 📇

Uber is one of the most famous startups in the world. It started as a ride-sharing application for people who couldn't afford a taxi. Now, Uber has expanded its activities to include Food Delivery with Uber Eats, package delivery, freight transportation, and even urban transportation with Jump Bike and Lime, companies that Uber has funded.

The company's goal is to revolutionize transportation across the globe. It operates in about 70 countries and 900 cities and generates over $14 billion in revenue! 😮

Project Overview 🚧

One of the main pain points that Uber's team found is that sometimes drivers are not around when users need them. Therefore, we are working on a project to identify hot-zones in NYC where drivers should be present at any given time of day.

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Machine Learning Projects: This repo includes 3 projects using Logistic Regression, XGBoost, Random Forest, Linear Regression, Lasso, Ridge, and DBSCAN. It covers both supervised and unsupervised learning techniques with detailed code and data preprocessing.

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