Pinned Repositories
Natural-Language-Processing-for-Amazon-Alexa-Reviews
The dataset consists of a nearly 3000 Amazon customer reviews (input text), star ratings, date of review, variant and feedback of various amazon Alexa products like Alexa Echo, Echo dots, Alexa Firesticks etc. for learning how to train Machine for sentiment analysis. We will use this data to analyze Amazon’s Alexa product ; discover insights into consumer reviews and assist with machine learning models. We will also train our machine models for sentiment analysis and analyze customer reviews on how many positive or negative reviews there are about this product.
IMDB-Movie-Analytics-and-Business-Intelligence
Used Talend, PowerBI, Tableau and Alteryx
Identify-and-Predict-Credit-Card-Defaulter---Advanced-Data-Science
Used Machine Learning algorithms like Logistic Regression, Random Forest and Decision Trees to create predictive models and interpret them. Found a significant relation for each algorithm in the data to create multivariate models.
Machine-Learning-from-disaster-on-Titanic-dataset
The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this Kaggle challenge, we are asked to build a predictive model which will answer the question: "What sorts of people are more likely to survive?" using the passenger data such as name,age,gender,socio-economic class etc.
NYPD_DataWarehouseAndBusinessIntelligence
Data Integration and BI visualizations for NYC Open Data
Sentimental-analysis-of-User-Reviews-in-the-Google-Playstore
• Cleaned the raw and unstructured data, removed the duplicates using Pandas obtained from the Playstore website • Analyzed the data popularity against price, size and the category of app and visualized the results using Seaborn and Matplotlib • Performed sentimental analysis using categorized user reviews and plotted sentimental polarity scores
Database-Design-and-Development-for-Fantasy-Football-Manager
SQL Server, Excel, HTML 5,CSS, JavaScript, Tableau, PowerBI
InstaCart--Super-Market-Analysis
Data Science final project for Instacart dataset retrieved through Kaggle to perform Market Basket Analysis
priyankabandekar31
Python_Codecademy
Python 3 Course on Codecademy with Projects
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