Predictive Analytics

Imbalanced Classes, Resampling techniques, filling null value, Date,Time, Alphanumeric data

Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.

Apply unsupervised learning techniques to identify customers segments.

Trying to predict which species are most threatened with extinction in the near future.

Car Price Prediction

Predict employee attrition using LogisticRegression and RandomForestClassifier.

Data cleaning tools, handling missing data, categorical data, feature scaling

Machine learning and neural networks used to create a binary classifier capable of predicting whether applicants will be successful if funded by Alphabet Soup.

Create a binary classification model using a deep neural network

pipelines chains together multiple steps so that the output of each step is used as input to the next step

To predict whether a user will click on ad or not.

scikit_learn

Embedding

In this exploratory data analysis, we compare a dataset which consists of various features about renting of houses available on these renting platforms listed by owners of these houses, and try to derive some constructive conclusions by performing Descriptive statistics of the available features.