Mental Health Fitness Tracker

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.

INSTALLATION

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 xgboost

Download the project files and navigate to the project directory.

USAGE

IMPORT THE NECESSARY LIBRARIES

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge, Lasso, ElasticNet, LinearRegressi from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegre from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error, r2_score
from xgboost import XGBRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.neural_network import MLPRegressor

READ THE DATA FROM THE CSV FILES

df1 = pd.read_csv('mental-and-substance-use-as-share-of-disease.csv')
df2 = pd.read_csv('prevalence-by-mental-and-substance-use-disorder.csv')
df1.head()
df2.head()

Merging two datasets prevalence-by-mental-and-substance-use-disorder.csv & mental-and-substance-use-as-share-of-disease.csv

data = pd.merge(df1, df2)
data.head(10)

Filling missing values in dataset

data.isnull().sum()
#drop the column
data.drop('Code', axis=1, inplace=True)
#view the data
data.head(10)
#size =row*column ,shape=tuple of array dimensions(row,col)
data.size,data.shape
#column set
data.set_axis(['Country','Year','Schizophrenia', 'Bipolar_disorder', 'Eating_disorder','Anxiety','drug_usage','depression','alcohol','mental_fitness'], axis='columns', inplace=True)
data.head(10) #our target or dependent if mental_fitness
plt.figure(figsize=(12,6))
sns.heatmap(data.corr(),annot=True,cmap='Greens')  #heatmap is defined as graphical representation of data using colors for visual representation of matrix
plt.plot()
sns.jointplot(data,x="Schizophrenia",y="mental_fitness",kind="reg",color="m")
plt.show()
sns.jointplot(data,x='Bipolar_disorder',y='mental_fitness',kind='reg',color='blue')
plt.show()
sns.pairplot(data,corner=True)  #paiwise relation ships in a dataset
plt.show()
mean = data['mental_fitness'].mean()
mean
fig = 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()

Information about the data

df.info()
#transform non-numeric labels to numeric labeles
from sklearn.preprocessing import LabelEncoder
l=LabelEncoder()
for i in df.columns:
    if df[i].dtype == 'object': #transform non-numerical labels (as long as they are hashable and comparable) to numeric labels
        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   #used to split the data into training data and testing data
xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=2)
#random_state simply set seeds to the random generator,so that your train test splits are always deterministic,if you don't set seed it will different each time
#tainning(6840,10)
#6840*80/100=5472
#6840*20/100=1368

Training and Testing

print("xtrain: ", xtrain.shape)
print("xtest: ", xtest.shape)
print("ytrain: ", ytrain.shape)
print("ytest: ", ytest.shape)

Linear Regression

from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
lr = LinearRegression()
lr.fit(xtrain,ytrain)   #fit trainng data

# model evaluation for training set
ytrain_pred = lr.predict(xtrain)
#the mean square error is the average of the square of the difference between observed and predicted value of a variable
mse = mean_squared_error(ytrain, ytrain_pred)   #observed value and predicted value
#root mean square error measures the average difference between values predicted by model and actua values
rmse = (np.sqrt(mean_squared_error(ytrain, ytrain_pred)))
#the coefficent of determination or R2,is a measure that priovides information about the goodness of fit of a model.In the context of regression it is a statistical measure oif
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")

Random Forest Regressor

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")
#linear regression model evaluation for testing set
ytest_pred = lr.predict(xtest)  # (unseen data)
mse = mean_squared_error(ytest, ytest_pred)
rmse = (np.sqrt(mean_squared_error(ytest, ytest_pred)))
r2 = r2_score(ytest, ytest_pred)

Final Output

print("linear regression model performance for testing set")
print("--------------------------------------")
print('MSE is {}'.format(mse))
print('RMSE is {}'.format(rmse))
print('R2 score is {}'.format(r2))
# random forest model evaluation for testing set
ytest_pred = rf.predict(xtest)   # (unseen data)
mse = mean_squared_error(ytest, ytest_pred)
rmse = (np.sqrt(mean_squared_error(ytest, ytest_pred)))
r2 = r2_score(ytest, ytest_pred)

print(" random forest model performance for testing set")
print("--------------------------------------")
print('MSE is {}'.format(mse))
print('RMSE is {}'.format(rmse))
print('R2 score is {}'.format(r2))