In this article, we will learn how to develop a machine learning model using Python which can predict the number of calories a person has burnt during a workout based on some biological measures.
Importing Libraries and Dataset
Python libraries make it easy for us to handle the data and perform typical and complex tasks with a single line of code.
- Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
- Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
- Matplotlib/Seaborn – This library is used to draw visualizations.
- Sklearn – This module contains multiple libraries are having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.
- XGBoost – This contains the eXtreme Gradient Boosting machine learning algorithm which is one of the algorithms which helps us to achieve high accuracy on predictions.
Python3
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sb from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder, StandardScaler from sklearn import metrics from sklearn.svm import SVC from xgboost import XGBRegressor from sklearn.linear_model import LinearRegression, Lasso, Ridge from sklearn.ensemble import RandomForestRegressor import warnings warnings.filterwarnings('ignore') |
Now let’s load the dataset into the panda’s data frame and print its first five rows.
Python3
df = pd.read_csv('calories.csv') df.head() |
Output:
First five rows of the dataset
Now let’s check the size of the dataset.
Python3
df.shape |
Output:
(15000, 9)
Let’s check which column of the dataset contains which type of data.
Python3
df.info() |
Output:
Information of the column’s data type
Now we will check the descriptive statistical measures of the data.
Python3
df.describe() |
Output:
Descriptive statistical measures of the dataset
Exploratory Data Analysis
EDA is an approach to analyzing the data using visual techniques. It is used to discover trends, and patterns, or to check assumptions with the help of statistical summaries and graphical representations.
Python3
sb.scatterplot(df['Height'], df['Weight']) plt.show() |
Output:
Scatterplot of height v/s weight
So, we have a kind of linear relationship between these two features which is quite obvious.
Python3
features = ['Age', 'Height', 'Weight', 'Duration'] plt.subplots(figsize=(15, 10)) for i, col in enumerate(features): plt.subplot(2, 2, i + 1) x = df.sample(1000) sb.scatterplot(x[col], x['Calories']) plt.tight_layout() plt.show() |
Output:
Scatter plot for features and target column
As expected higher is the duration of the workout higher will be the calories burnt. But except for that, we cannot observe any such relation between calories burnt and height or weight features.
Here we can observe some real-life observations:
- The average height of the boys is higher than girls.
- Also, the weight of the girls is lower than that of the boys.
- For the same average duration of workout calories burnt by men is higher than that of women.
Python3
features = df.select_dtypes(include='float').columns plt.subplots(figsize=(15, 10)) for i, col in enumerate(features): plt.subplot(2, 3, i + 1) sb.distplot(df[col]) plt.tight_layout() plt.show() |
Output:
Distribution plot for continuous features
The distribution of the continuous features follows close to normal distribution except for some features like Body_Temp and Calories.
Python3
df.replace({'male': 0, 'female': 1}, inplace=True) df.head() |
Output:
First five rows of the dataset
Python3
plt.figure(figsize=(8, 8)) sb.heatmap(df.corr() > 0.9, annot=True, cbar=False) plt.show() |
Output:
Heatmap to detect highly correlated features
Here we have a serious problem of data leakage as there is a feature that is highly correlated with the target column which is calories.
Python3
to_remove = ['Weight', 'Duration'] df.drop(to_remove, axis=1, inplace=True) |
Model Training
Now we will separate the features and target variables and split them into training and testing data by using which we will select the model which is performing best on the validation data.
Python3
features = df.drop(['User_ID', 'Calories'], axis=1) target = df['Calories'].values X_train, X_val,\ Y_train, Y_val = train_test_split(features, target, test_size=0.1, random_state=22) X_train.shape, X_val.shape |
Output:
((13500, 5), (1500, 5))
Now, let’s normalize the data to obtain stable and fast training.
Python3
# Normalizing the features for stable and fast training. scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_val = scaler.transform(X_val) |
Now let’s train some state-of-the-art machine learning models and compare them which fit better with our data.
Python3
from sklearn.metrics import mean_absolute_error as mae models = [LinearRegression(), XGBRegressor(), Lasso(), RandomForestRegressor(), Ridge()] for i in range(5): models[i].fit(X_train, Y_train) print(f'{models[i]} : ') train_preds = models[i].predict(X_train) print('Training Error : ', mae(Y_train, train_preds)) val_preds = models[i].predict(X_val) print('Validation Error : ', mae(Y_val, val_preds)) print() |
Output:
LinearRegression() : Training Error : 17.893463692619434 Validation Error : 18.007896272831253 XGBRegressor() : Training Error : 10.110870876925963 Validation Error : 10.16210130894184 Lasso() : Training Error : 17.915089584958036 Validation Error : 17.995033362288662 RandomForestRegressor() : Training Error : 3.982735208112875 Validation Error : 10.472395222222223 Ridge() : Training Error : 17.893530494767777 Validation Error : 18.00781790803129
Out of all the above models, we have trained RandomForestRegressor and the XGB model’s performance is the same as their MAE for the validation data is same.
