- Exploring the NYC Airbnb MarketExploring the NYC Airbnb Market
NYC is one of the most-visited cities in the world. As a result, there are many Airbnb listings to meet the high demand for temporary lodging for anywhere between a few nights to many months. This project involved data importing and cleaning skills to uncover insights into the Airbnb market in New York City.
• Importing the Data
• Cleaning the price column
• Calculating average price
• Comparing costs to the private rental market
• Cleaning the room-type column
• Calculating the timeframe of working
• Joining the DataFrames.
• Analyzing listing prices by NYC borough
• Price range by borough
- Predicting Credit Card ApprovalsPredicting Credit Card Approvals
Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low-income levels, or too many inquiries on an individual's credit report, example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money). In this project, I have built an automatic credit card approval predictor using machine learning techniques, just like real banks do.
• Credit card applications
• Inspecting the applications
• Splitting the dataset into train and test sets
• Handling the missing values
• Preprocessing the data
• Fitting a logistic regression model to the train set
• Making predictions and evaluating performance
• Grid searching and making the model perform better
• Finding the best performing model
- Uncover the Trendiest Topics in ML Research
Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world where groundbreaking work is published. In this project, I analyzed an extensive collection of NIPS research papers from the past decade to discover the latest trends in machine learning.
• Loading the NIPS papers
• Preparing the data for analysis
• Plotting how machine learning has evolved over time
• Preprocessing the text data
• A word cloud to visualize the preprocessed text data
• Prepared the text for LDA analysis
• Analysing trends with LDA
- Insurance Prediction with Machine Learning using Python
My task is to train a machine learning model to predict whether an individual will purchase the insurance policy from the company or not
I did The following task -
• EDA
• Numerical Data Exploration
• Categorical Data Exploration
• Correlation Test
• Significant Test
• Splitting the dataset
• Scaling the data
• Model Building with Logistic Regression, SVM, KNN, Naïve Based, SGD Classifier, Decision Tree, Random Forest, XG Boost
• Choosing the Best Model