Building a Machine Learning algorithm is more than just feeding data into it; several flaws impair any model’s performance. Overfitting in Machine Learning is one such flaw that reduces the model’s accuracy and efficiency.

When we feed a mathematical model with much more data (including noise) than it takes, it is overfitted. Consider wanting to blend into bulky clothing to make it more relatable. As a model suits more data than it does, it begins to detect noisy data and incorrect values in the data. As a consequence, the model’s performance and consistency suffer.

Training with more data will help…

In this lab, I completed a series of exercises exploring movie rating data IMDb. I conducted basic exploratory data analysis on IMDB’s movie data, looking to answer such questions as what is the average rating per genre? How many different actors are in a movie?

Basic level

Let’s import the necessary libraries:

Read in ‘imdb_1000.csv’ and store it in a DataFrame named ‘movies’.

Introduction

When analyzing your data, it is crucial to recognize and understand the importance of each data type. Depending on the type of your data, a specific analysis will be appropriate. Similarly, the data type will also drive the choice of data visualization techniques. In this series of 2 posts, I first presented the different type of statistical data and provided some examples of each type. I further explained the importance of data types using Python and R programing.

In this part 1, I covered data type with Python, converting data type with Python, and visualizing quantitative and qualitative.

Numerical data Versus categorical data

Various data…