In this project, we will apply Exploratory Data Analysis to describe features and apply some statistical techniques. Also, we will perform a Linear Regression model in order to predict a variable from our data.
We choose the Car Price Prediction Data from Kaggle to work on this project. Here are the steps that we are going to perform:
📌 Exploring the data and applying descriptive statistics methods.
* Brief information about the data.
* Apply some python codes to describe the data.
* Picking one qualitative and one quantitative variable describing them by using the convenience statistical methods.
* Applying data visualization in order to get better understanding about the dispersion of these two variables selected before.
* Providing a description about the distribution of variables based on the descriptive statistics and visualizations that we applied before.
* Checking the missing values.
* Examining the outliers. 📌 Hypothesis Test.
* Choosing one variable from the data and performing a Hypothesis Test by supporting all the steps with appropriate references, statistical concepts and conclusions.
* Interpreting the results, prowiding our own analysis and a conclusion based on our Hypothesis Test.
📌 Correlation Analysis
* Applying a correlation analysis between 2 variables.
* Interpreting the results and checking if the correlation implies causation.
* Providing a conclusion based on the findings. 📌 Linear Regression Model
* Building linear regression models for prediction.
* Interpreting the results and providing a conclusion based on the findings. This project was applied togather with @MilaSoul.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent