Car Price Prediction
-
Updated
Apr 4, 2021 - Jupyter Notebook
Car Price Prediction
Apply unsupervised learning techniques to identify customers segments.
Data cleaning tools, handling missing data, categorical data, feature scaling
Predict employee attrition using LogisticRegression and RandomForestClassifier.
pipelines chains together multiple steps so that the output of each step is used as input to the next step
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.
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.
Embedding
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
Predictive Analytics
Imbalanced Classes, Resampling techniques, filling null value, Date,Time, Alphanumeric data
Add a description, image, and links to the onehotencoder topic page so that developers can more easily learn about it.
To associate your repository with the onehotencoder topic, visit your repo's landing page and select "manage topics."