The Mental Health Fitness Tracker project focuses on analyzing and predicting mental fitness levels of individuals from various countries with different mental disorders. It utilizes regression techniques to provide insights into mental health and make predictions based on the available data. The project also provides a platform for users to track their mental health and fitness levels. The project is built using Python.
To use the code and run the examples, follow these steps:
- Ensure that you have Python 3.x installed on your system.
- Install the required libraries by running the following command:
pip install pandas numpy matplotlib seaborn scikit-learn plotly.express- Download the project files and navigate to the project directory.
- Select the country and the mental disorder you want to analyze.
- Select the year range you want to analyze.
- Click on the "Analyze" button.
- The app will display the results of the analysis.
- Click on the "Predict" button to predict the mental fitness level of the selected country.
- The app will display the predicted mental fitness level of the selected country.
- Click on the "Track" button to track your mental fitness level.
- The app will display the results of the tracking.
Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the repo
- Clone the project
- Create your feature branch
- Commit your changes
- Push to the branch
- Open a pull request
- Datasets that were useD in here were taken from KAGGLE
- This project was made during my internship period for Edunet Foundation in association with IBM SkillsBuild and AICTE
[](https://colab.research.google.com/drive/1p73IXLADeEAJ5p1zHfVtmMgvzyhhLYgW?usp=drive_link)import pandas as pd
import numpy as npimport seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import warnings
warnings.filterwarnings('ignore')df1 = pd.read_csv("prevalence-by-mental-and-substance-use-disorder.csv")
df2 = pd.read_csv('mental-and-substance-use-as-share-of-disease.csv')df1.head()df2.head()data = pd.merge(df1, df2)
data.head()data.isnull().sum()data.drop('Code', axis=1, inplace=True)data.size,data.shapedata.set_axis(['Country','Year','Schizophrenia', 'Bipolar_disorder', 'Eating_disorder','Anxiety','drug_usage','depression','alcohol','mental_fitness'], axis='columns', inplace=True)
data.head()plt.figure(figsize=(12,6))
sns.heatmap(data.corr(),annot=True,cmap='coolwarm')
plt.plot()sns.jointplot(data,x="Schizophrenia",y="mental_fitness",kind="reg",color="g")
plt.show()sns.jointplot(data,x='Bipolar_disorder',y='mental_fitness',kind='reg',color='m')
plt.show()sns.jointplot(data,x='Eating_disorder',y='mental_fitness',kind='reg',color='blue')
plt.show()sns.jointplot(data,x='Anxiety',y='mental_fitness',kind='reg',color='y')
plt.show()sns.jointplot(data,x='drug_usage',y='mental_fitness',kind='reg',color='r')
plt.show()sns.jointplot(data,x='depression',y='mental_fitness',kind='reg',color='orange')
plt.show()sns.jointplot(data,x='alcohol',y='mental_fitness',kind='reg',color='black')
plt.show()sns.pairplot(data,corner=True)
plt.show()mean = data['mental_fitness'].mean()
meanfig = px.pie(data, values='mental_fitness', names='Year')
fig.show()fig=px.bar(data.head(10),x='Year',y='mental_fitness',color='Year',template='ggplot2')
fig.show()fig = px.line(data, x="Year", y="mental_fitness", color='Country',markers=True,color_discrete_sequence=['red','blue'],template='plotly_dark')
fig.show()df=data.copy()
df.head()df.info()from sklearn.preprocessing import LabelEncoder
l=LabelEncoder()
for i in df.columns:
if df[i].dtype == 'object':
df[i]=l.fit_transform(df[i])
X = df.drop('mental_fitness',axis=1)
y = df['mental_fitness']from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=2)X = df.drop('mental_fitness',axis=1)
y = df['mental_fitness']from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=2)print("xtrain: ", xtrain.shape)
print("xtest: ", xtest.shape)
print("ytrain: ", ytrain.shape)
print("ytest: ", ytest.shape)from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
lr = LinearRegression()
lr.fit(xtrain,ytrain)
# model evaluation for training set
ytrain_pred = lr.predict(xtrain)
mse = mean_squared_error(ytrain, ytrain_pred)
rmse = (np.sqrt(mean_squared_error(ytrain, ytrain_pred)))
r2 = r2_score(ytrain, ytrain_pred)
print("The model performance for training set")
print("--------------------------------------")
print('MSE is {}'.format(mse))
print('RMSE is {}'.format(rmse))
print('R2 score is {}'.format(r2))
print("\n")
# model evaluation for testing set
ytest_pred = lr.predict(xtest)
mse = mean_squared_error(ytest, ytest_pred)
rmse = (np.sqrt(mean_squared_error(ytest, ytest_pred)))
r2 = r2_score(ytest, ytest_pred)
print("The model performance for testing set")
print("--------------------------------------")
print('MSE is {}'.format(mse))
print('RMSE is {}'.format(rmse))
print('R2 score is {}'.format(r2))from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor()
rf.fit(xtrain, ytrain)
# model evaluation for training set
ytrain_pred = rf.predict(xtrain)
mse = mean_squared_error(ytrain, ytrain_pred)
rmse = (np.sqrt(mean_squared_error(ytrain, ytrain_pred)))
r2 = r2_score(ytrain, ytrain_pred)
print("The model performance for training set")
print("--------------------------------------")
print('MSE is {}'.format(mse))
print('RMSE is {}'.format(rmse))
print('R2 score is {}'.format(r2))
print("\n")
# model evaluation for testing set
ytest_pred = rf.predict(xtest)
mse = mean_squared_error(ytest, ytest_pred)
rmse = (np.sqrt(mean_squared_error(ytest, ytest_pred)))
r2 = r2_score(ytest, ytest_pred)
print("The model performance for testing set")
print("--------------------------------------")
print('MSE is {}'.format(mse))
print('RMSE is {}'.format(rmse))
print('R2 score is {}'.format(r2))np.random.seed(range(0, 100))
print("Welcome to Mental Fitness Tracker!\nPlease enter the details below to get started-->")
country = l.fit_transform([input("Enter the country name: ")])
year = int(input("Enter the year: "))
schizophrenia = (float(input("Enter the Schizophrenia rate in %(if not enter 0): ")))/100
bipolar_disorder = (float(input("Enter the Bipolar Disorder rate in %(if not enter 0): ")))/100
eating_disorder = (float(input("Enter the Eating Disorder rate in %(if not enter 0): ")))/100
anxiety = (float(input("Enter the Anxiety rate in %(if not enter 0): ")))/10
drug_usage = (float(input("Enter the Drug Usage rate in %(if not enter 0): ")))/100
depression = (float(input("Enter the Depression rate in %(if not enter 0): ")))/10
alcohol = (float(input("Enter the Alcohol rate in %(if not enter 0): ")))/100
data = [[country, year, schizophrenia, bipolar_disorder, eating_disorder, anxiety, drug_usage, depression, alcohol]]
data = pd.DataFrame(data, columns=['Country', 'Year', 'Schizophrenia', 'Bipolar_disorder', 'Eating_disorder', 'Anxiety', 'drug_usage', 'depression', 'alcohol'])
data['mental_fitness'] = rf.predict(data)
print("Mental fitness score in percent is: ", (data['mental_fitness'][0])*10)The mental fitness of a person is dependent on various factors like schizophrenia, bipolar disorder, eating disorder, anxiety, drug usage, depression, alcohol, etc.
Here, we have used the Random Forest Regressor model to predict the mental fitness based on the above factors. The model has an accuracy of 99.99% which is quite good. The model can be used to predict the mental fitness. The model can be further improved by using more data and by using other algorithms.
Distributed under the ICS License. See LICENSE for more information.