Project 16 Calories Burnt Prediction

තවත් සම්පූර්ණ regression project එකක් — ඇත්තම fluency එකක් හදාගන්න, අලුත්, නොදන්නා dataset එකකට එකම workflow එක (clean, split, train, evaluate) හිතාමතාම නැවත කරනවා.

Another full regression project — deliberate repetition of the same workflow (clean, split, train, evaluate) on a fresh, unfamiliar dataset to build real fluency.

17 May 20267 min readSelf Guide · Stage 16#Evaluating & Choosing Models#Hands-on Projects#Machine learning

Lesson 50 of 64 · Part 4 · Evaluating & Choosing Models · Hands-on Projects · bilingual notes (English + සිංහල)

Why this matters. Another full regression project — deliberate repetition of the same workflow (clean, split, train, evaluate) on a fresh, unfamiliar dataset to build real fluency.

සිංහලෙන්: තවත් සම්පූර්ණ regression project එකක් — ඇත්තම fluency එකක් හදාගන්න, අලුත්, නොදන්නා dataset එකකට එකම workflow එක (clean, split, train, evaluate) හිතාමතාම නැවත කරනවා.

What you'll use this for. Fitness apps that estimate calories burnt from your workout stats use exactly this kind of regression model behind the scenes.

සිංහලෙන්: ඔයාගෙ workout stats වලින් calories burnt estimate කරන fitness apps, screen එකට පිටිපස්සෙන් වැඩ කරන්නෙ හරියටම මේ වගේ regression model එකකින්.

Key principles

Fluency comes from repetition on unfamiliar data, not from memorizing one dataset. If you can run this same five-step pipeline on a dataset you've never seen before without looking anything up, that's the real signal you've learned the process, not just this one example.
Fluency එක ලැබෙන්නෙ නොදන්නා data එකට repetition එකෙන්, එක dataset එකක් memorize කරන එකෙන් නෙවෙයි

Walkthrough

Importing the Dependencies

ExamplePython

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
from sklearn import metrics

Data Collection & Processing

ExamplePython

# loading the data from csv file to a Pandas DataFrame
calories = pd.read_csv('/content/calories.csv')

ExamplePython

# print the first 5 rows of the dataframe
calories.head()

	User_ID	Calories
0	14733363	231.0
1	14861698	66.0
2	11179863	26.0
3	16180408	71.0
4	17771927	35.0

ExamplePython

exercise_data = pd.read_csv('/content/exercise.csv')

ExamplePython

exercise_data.head()

	User_ID	Gender	Age	Height	Weight	Duration	Heart_Rate	Body_Temp
0	14733363	male	68	190.0	94.0	29.0	105.0	40.8
1	14861698	female	20	166.0	60.0	14.0	94.0	40.3
2	11179863	male	69	179.0	79.0	5.0	88.0	38.7
3	16180408	female	34	179.0	71.0	13.0	100.0	40.5
4	17771927	female	27	154.0	58.0	10.0	81.0	39.8

Combining the two Dataframes

ExamplePython

calories_data = pd.concat([exercise_data, calories['Calories']], axis=1)

ExamplePython

calories_data.head()

	User_ID	Gender	Age	Height	Weight	Duration	Heart_Rate	…
0	14733363	male	68	190.0	94.0	29.0	105.0	…
1	14861698	female	20	166.0	60.0	14.0	94.0	…
2	11179863	male	69	179.0	79.0	5.0	88.0	…
3	16180408	female	34	179.0	71.0	13.0	100.0	…
4	17771927	female	27	154.0	58.0	10.0	81.0	…

ExamplePython

# checking the number of rows and columns
calories_data.shape

Output

Output

(15000, 9)

ExamplePython

# getting some informations about the data
calories_data.info()

Output

Output

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 15000 entries, 0 to 14999
Data columns (total 9 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   User_ID     15000 non-null  int64  
 1   Gender      15000 non-null  object 
 2   Age         15000 non-null  int64  
 3   Height      15000 non-null  float64
 4   Weight      15000 non-null  float64
 5   Duration    15000 non-null  float64
 6   Heart_Rate  15000 non-null  float64
 7   Body_Temp   15000 non-null  float64
 8   Calories    15000 non-null  float64
dtypes: float64(6), int64(2), object(1)
memory usage: 1.0+ MB

ExamplePython

# checking for missing values
calories_data.isnull().sum()

Output

Output

User_ID       0
Gender        0
Age           0
Height        0
Weight        0
Duration      0
Heart_Rate    0
Body_Temp     0
Calories      0
dtype: int64

Data Analysis

ExamplePython

# get some statistical measures about the data
calories_data.describe()

	User_ID	Age	Height	Weight	Duration	Heart_Rate	Body_Temp	Calories
count	1.500000e+04	15000.000000	15000.000000	15000.000000	15000.000000	15000.000000	15000.000000	15000.000000
mean	1.497736e+07	42.789800	174.465133	74.966867	15.530600	95.518533	40.025453	89.539533
std	2.872851e+06	16.980264	14.258114	15.035657	8.319203	9.583328	0.779230	62.456978
min	1.000116e+07	20.000000	123.000000	36.000000	1.000000	67.000000	37.100000	1.000000
25%	1.247419e+07	28.000000	164.000000	63.000000	8.000000	88.000000	39.600000	35.000000
50%	1.499728e+07	39.000000	175.000000	74.000000	16.000000	96.000000	40.200000	79.000000

… 2 more rows

Data Visualization

ExamplePython

sns.set()

ExamplePython

# plotting the gender column in count plot
sns.countplot(calories_data['Gender'])

Output

Output

/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only va
  FutureWarning

Output

Output

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbbd756110>

Project 16 Calories Burnt Prediction — figure 1

ExamplePython

# finding the distribution of "Age" column
sns.distplot(calories_data['Age'])

Output

Output

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version
  warnings.warn(msg, FutureWarning)

Output

Output

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbbd200550>

Project 16 Calories Burnt Prediction — figure 2

ExamplePython

# finding the distribution of "Height" column
sns.distplot(calories_data['Height'])

Output

Output

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version
  warnings.warn(msg, FutureWarning)

Output

Output

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbb1ed3d10>

Project 16 Calories Burnt Prediction — figure 3

ExamplePython

# finding the distribution of "Weight" column
sns.distplot(calories_data['Weight'])

Output

Output

/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version
  warnings.warn(msg, FutureWarning)

Output

Output

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbb1e2c190>

Project 16 Calories Burnt Prediction — figure 4

Finding the Correlation in the dataset

Positive Correlation
Negative Correlation

ExamplePython

correlation = calories_data.corr()

ExamplePython

# constructing a heatmap to understand the correlation

plt.figure(figsize=(10,10))
sns.heatmap(correlation, cbar=True, square=True, fmt='.1f', annot=True, annot_kws={'size':8}, cmap='Blues')

Output

Output

<matplotlib.axes._subplots.AxesSubplot at 0x7fcbd5c75650>

Project 16 Calories Burnt Prediction — figure 5

Converting the text data to numerical values

ExamplePython

calories_data.replace({"Gender":{'male':0,'female':1}}, inplace=True)

ExamplePython

calories_data.head()

	User_ID	Gender	Age	Height	Weight	Duration	Heart_Rate	…
0	14733363	0	68	190.0	94.0	29.0	105.0	…
1	14861698	1	20	166.0	60.0	14.0	94.0	…
2	11179863	0	69	179.0	79.0	5.0	88.0	…
3	16180408	1	34	179.0	71.0	13.0	100.0	…
4	17771927	1	27	154.0	58.0	10.0	81.0	…

Separating features and Target

ExamplePython

X = calories_data.drop(columns=['User_ID','Calories'], axis=1)
Y = calories_data['Calories']

ExamplePython

print(X)

Output

Output

       Gender  Age  Height  Weight  Duration  Heart_Rate  Body_Temp
0           0   68   190.0    94.0      29.0       105.0       40.8
1           1   20   166.0    60.0      14.0        94.0       40.3
2           0   69   179.0    79.0       5.0        88.0       38.7
3           1   34   179.0    71.0      13.0       100.0       40.5
4           1   27   154.0    58.0      10.0        81.0       39.8
...       ...  ...     ...     ...       ...         ...        ...
14995       1   20   193.0    86.0      11.0        92.0       40.4
14996       1   27   165.0    65.0       6.0        85.0       39.2
14997       1   43   159.0    58.0      16.0        90.0       40.1
14998       0   78   193.0    97.0       2.0        84.0       38.3
14999       0   63   173.0    79.0      18.0        92.0       40.5

[15000 rows x 7 columns]

ExamplePython

print(Y)

Output

Output

0        231.0
1         66.0
2         26.0
3         71.0
4         35.0
         ...  
14995     45.0
14996     23.0
14997     75.0
14998     11.0
14999     98.0
Name: Calories, Length: 15000, dtype: float64

Splitting the data into training data and Test data

ExamplePython

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)

ExamplePython

print(X.shape, X_train.shape, X_test.shape)

Output

Output

(15000, 7) (12000, 7) (3000, 7)

Model Training

XGBoost Regressor

ExamplePython

# loading the model
model = XGBRegressor()

ExamplePython

# training the model with X_train
model.fit(X_train, Y_train)

Output

Output

[10:06:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.

Output

Output

XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
             colsample_bynode=1, colsample_bytree=1, gamma=0,
             importance_type='gain', learning_rate=0.1, max_delta_step=0,
             max_depth=3, min_child_weight=1, missing=None, n_estimators=100,
             n_jobs=1, nthread=None, objective='reg:linear', random_state=0,
             reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,
             silent=None, subsample=1, verbosity=1)

Evaluation

Prediction on Test Data

ExamplePython

test_data_prediction = model.predict(X_test)

ExamplePython

print(test_data_prediction)

Output

Output

[129.06204  223.79721   39.181965 ... 145.59767   22.53474   92.29064 ]

Mean Absolute Error

ExamplePython

mae = metrics.mean_absolute_error(Y_test, test_data_prediction)

ExamplePython

print("Mean Absolute Error = ", mae)

Output

Output

Mean Absolute Error =  2.7159012502233186

Keep going: ← Lesson 49 · Lesson 51 → · Track it in the Self Guide

Finished this one?

Part of Self Guide · Stage 16: ML 4 — Evaluating & Choosing Models. Progress is saved in this browser.

Guide

Previous Next

Projects in this lesson

ML & Vision

Machine Learning Playbook & Deep Learning Lab

Interactive machine learning educational playground and hands-on laboratory covering computer vision, regression, and deep neural nets.

Project 16 Calories Burnt Prediction

Key principles

Walkthrough

Importing the Dependencies

Data Collection & Processing

Combining the two Dataframes

Data Analysis

Data Visualization

Finding the Correlation in the dataset

Converting the text data to numerical values

Separating features and Target

Splitting the data into training data and Test data

Model Training

XGBoost Regressor

Evaluation

Prediction on Test Data

Mean Absolute Error

Machine Learning Playbook & Deep Learning Lab

Teaching Networking, DevOps and ML in Sinhala

Zero-Cost Hosting for InFocus SLUG 2025 — and 25 Hosting & Domain Options Compared

Life Vision: The Distributed Life