This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price
In this project I'm trying to analyze and visualize the Used Car Prices from the dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data in order to predict the most probable car price
It is divided into four parts:
Data Wrangling
Exploratory Data Analysis
Model Development
Model Review and Evaluation
- Anaconda Distribution
- Jupyter Notebook
- Numpy
- Pandas
- Matplotlib
- Seaborn
import pandas as pd
import numpy as np
import math
import matplotlib.pyplot as plt
import seaborn as sns
Histograms representing Binned prices in Low, Medium, High
Boxplots representing effect of wheel frive with prices.
Scatter plot for Prices over Engine size
Pivot table categorizing wheel drive and body style with prices.
HeatMap with wheel drive in y axis and body style in x axis.
Positive Linear Relationship between engine size and price
Negetive Linear Relationship between highway-mpg and price
Weak Correlation between peak-rpm and price
Simple Linear Regression plot
Multiple Linear Regression plot
The distribution plot of Linear Regression and Multiple Regression technique shows how the model predicts the prices of automobiles based on "horsepower", "curb-weight", "engine-size" and "highway-mpg"
Comparing these three models, we conclude that the MLR model is the best model to be able to predict price from our dataset. This result makes sense, since we have 27 variables in total, and we know that more than one of those variables are potential predictors of the final car price.