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Hypothesis testing using data analytics for yellow trip car ride provider service to increase their revenue

A comprehensive machine learning model featuring behavioral pattern analysis and mobile device market segmentation using statistical methods, hypothesis testing, and unsupervised learning techniques. Built with Python, this project demonstrates practical applications of data extraction, preprocessing, and advanced analytics.

Used Hypothesis Testing to gather analytical insights about Manhattan Yelp and Inspection Grade data. Additionally, Used Machine Learning Classification techniques such as Logistic Regression, Decision Tree, Random Forest, XGBoost, and Adaboost, to predict Yelp ratings for Manhattan restaurants.

Hands-on journey into Statistics for Data Analysis using Python. Covers descriptive & inferential statistics, probability, hypothesis testing, regression, and data visualization. Designed as part of my Data Analytics portfolio to demonstrate problem-solving, actionable insights, and data-driven business decision-making.

Here in this project I have used various hypothesis testing and also done some descriptive analytics to understand the data.I used feature selection techniques to select the required number of features and at last I have built some machine learning models to predict whether a person has heart disease or not.

Uber is a transportation company with an app that allows passengers to hail a ride and drivers to charge fares and get paid. The platform links you to drivers who can take you to your destination. Since the industry is booming and expected to grow shortly, effective taxi dispatching will facilitate each driver and passenger to reduce the wait time to seek out one another. We evaluate the model's viability as an analytical tool first by cleaning the data followed by an Exploratory data analysis (EDA). EDA is used to analyse and investigate the data set and summarise its main characteristics, often employing data visualisation methods. It helps determine how best to manipulate data sources making it easier to discover patterns, spot anomalies, test a hypothesis, or check assumptions for large datasets. Our dataset includes primary data on Uber pickups, destination, duration of journey, distance commuted, computed fare, driver ratings and status of the rides taken. By applying various data cleaning and visualisation techniques to this data, we can analyse commuters'/customers' usage patterns.

A data analytics project comparing Facebook Ads and Google AdWords campaign effectiveness using statistical methods, hypothesis testing, and regression analysis to deliver actionable insights for digital marketing optimization. It demonstrates business-focused analytics, visualization, and data-driven recommendations for marketing strategy.

Most paths to purchase are not a straight line. There are lots of ways for customers to discover your brand, engage with it, and move further down the sales funnel. That’s why attribution models have become a necessary tool for marketers looking for data to improve their campaigns. Attribution modeling is a strategy that allows marketers to analyze and assign credit to marketing touchpoints that occur at the specific steps of the customer journey, from searching for a product online to making a purchase, and every action in between. Using attribution models helps marketers better understand which parts of their marketing efforts are driving the most leads to that part of the sales funnel[1]. In this project, we try to understand if there is a significant difference in Data of Google Merchandising Store using the Model Comparison Tool of Google Analytics. Here we implement Hypothesis Testing - ANOVA by comparing various models across a single channel. If there is a presence of Significant Difference after ANOVA implementation we then perform Tukey HSD (Honestly Significant Difference) Test to understand exactly which means are different i.e. because of which Attribution Models it delivers the Significant Difference. The result of our analysis would be to deliver if there is any presence of Statistical Significance in Google Merchandising Store Attribution Modelling Data.

This project aims to analyze churn of the students for Excelerate company using Data Analytics (EDA, Data Visualization, Hypothesis Testing), and AI models including Machine Learning and Deep Learning to give suitable recommendations to reduce the churn.

A collection of academic projects completed during my coursework at Northeastern University, showcasing applications of data analytics, statistics, and machine learning. Includes projects on regression modeling, hypothesis testing, regularization techniques, and exploratory data analysis using Python and R

A complete end-to-end data analytics project analyzing Airbnb markets in France and Seattle using Python, SQL, Tableau, Excel, statistical hypothesis testing, and machine learning to uncover insights about pricing, demand, guest satisfaction, and host performance

Interactive Streamlit analytics app using Python,Plotly,Pandas,NumPy and SciPy for EDA, hypothesis testing(T-test, ANOVA, Chi-square), segmentation and high-quality visualizations. Includes statistical tests, preprocessing and dashboards for data analysis, business insights and micro-mobility use cases for inventory management and demand prediction

Power-Bi-Project Supermarket Sales Data Analysis Description: To test a hypothesis or check assumptions related to different types of problems for business analytics using the supermarket sales data. The dataset is one of the historical sales of supermarket company which has recorded in three different branches for three months.

This project conducts a comprehensive analysis of Tesla's stock price trends using various statistical and analytical techniques. The dataset consists of historical Tesla stock data, including opening price, closing price, high, low, trading volume, and date information. The analysis involves descriptive statistics, hypothesis testing, probability

Seasoned team player with 6+ years progressive experience in supervising, data entry and collection, debt recovery and credit control. Instrumental in boosting efficiency, streamlining processes and cutting overhead expenses. Dedicated to cultivating high performance teams and promoting positive work environments. Proficient in ERP Material Management and Delivery modules of SAP,CRM,SPSS as well as MS office applications; with extensive working experience in Auctioneering, Banking and Sacco sector. I am endowed with the following skills and competences: • Analyze data using mathematical models and statistical techniques in class • Prepare collection reports. • Data entry-entering tenants in the system while at Mwanzo Properties. • Well-developed organizational skills and the ability to meet deadlines. • Integrity and ability to work in consumer finance with small scale • farmers/businesspersons across Kenya and outside. • Sound verbal communication skills include the ability to negotiate respectfully and communicate the rationale for decisions made. • Target driven, self-starter, and problem solver. • Demonstrated ability to follow policy and procedures. • Sound judgment, analytical, and decision-making skills. • Accuracy and attention to detail. • Demonstrated ability to quickly learn new systems and processes. • Demonstrated commitment to service excellence. • Proficiency in G-Suite, Microsoft Office. Explored the following fields: Operation Research, Statistical Methods in Econometrics, Time Series Analysis , Multivariate Analysis, Test of Hypothesis, Monitoring and Evaluation, Quality Control and Acceptance Sampling, Theory of Estimation, Bayesian Statistics, Sequential Analysis, Categorical data analysis, Sample Theory, Numerical Analysis, Experimental Design , and Parametric & Non-Parametric Methods.

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This project explores the relationship between payment methods and total revenue using data analytics and hypothesis testing. It applies descriptive statistics and statistical inference to determine whether payment type (cash vs. credit card) influences fare amounts and overall revenue.

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Hypothesis testing project using data analytics techniques to validate assumptions and draw statistical conclusions.

This is a hypothesis testing project that I've created to perform some analytics and statistical test to test hypothesis regarding the revenue increasing for cab provider named yellow trip as per their 2020 january data.

Hypothesis testing using data analytics for yellow trip car ride provider service to increase their revenue

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I've performed these skills using Excel here - Data Analytics, Hypothesis Testing, Data Visualization, Hypothesis Testing, Statistics, Forecasting, Excel, Regression, Monte Carlo Simulation, Linear Programming, Pivot Tables, Business Intelligence, Excel Analytics

End-to-end statistical analysis of global cancer patient data using Python, EDA, hypothesis testing, and inferential analytics.

Advanced data analytics & predictive modeling for bank marketing campaign optimization using statistical analysis, hypothesis testing, and machine learning

From Assumptions to Evidence: A Hypothesis-Driven Analytics Project An end-to-end analytics project that converts customer assumptions into statistically validated insights using survey data, correlation analysis, and hypothesis testing.

Data Mining through Visuilization, Hypothesis Testing. Also Predicitve Analytics/Machine Learning through Classification Tree. Using libraries ggplot2, rpart, rpart.plot