Project: Predicting Used Car Prices with Random Forests
This is for those who want to get into data science, who have a little bit of knowledge but are having a hard time coming up with your first data science project. That’s why I created a step by step guide to completing your first end-to-end machine learning model! Follow along, and you’ll learn a wide range of new skills. I used Kaggle’s used car data set because it had a variety of categorical and numerical data and allows you to explore different ways of dealing with missing data. I divided out my project into three parts: Exploratory Data Analysis Data Modelling Feature Importance 1. Exploratory Data Analysis Understanding my Data # Importing Libraries and Data import numpy as np import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) df = pd.read_csv("../input/craigslist-carstruck...