#1 Importing Needed Libraries 

import pandas as pd

import seaborn as sns

import numpy as np

from matplotlib import pyplot as plt

from sklearn.compose import ColumnTransformer

from sklearn.pipeline import Pipeline

from sklearn.experimental import enable_iterative_imputer

from sklearn.impute import SimpleImputer, IterativeImputer

from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder

from sklearn.model_selection import cross_val_score

from scipy import stats

from sklearn.cluster import AgglomerativeClustering

from scipy.cluster.hierarchy import linkage, dendrogram, fcluster

from sklearn.decomposition import PCA

from sklearn.model_selection import train_test_split

from sklearn.cluster import KMeans

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier

from sklearn.svm import SVC

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score

from keras.models import Model

from keras.layers import Input, Dense

import missingno as msno

#2 Loading and Exploring the Data

patient_df = pd.read_csv ('patients_01.csv')

#Exploring the Dataset

print(patient_df.describe())

print(patient_df.info())

# Number of samples

num_rows = len(patient_df)

print(num_rows)

# Identify the number of missing values in each feature

missing_values = patient_df.isnull().sum()

print(missing_values)

# Calculating the percentage of missing values for each feature

round(100*(1-patient_df.count()/len(patient_df)),2)

# Extracting samples with 3 or more missing values

patient_df.loc[patient_df.isnull().sum(axis=1)>=3, :]

# Extracting samples with 4 or more missing values

patient_df.loc[patient_df.isnull().sum(axis=1)>=4, :]

#3 Data Visualisation 

#3.1 Plotting the distribution of age

plt.figure(figsize=(10, 6))

# Plotting the histogram with specified bins and color

plt.hist(patient_df['age'], bins=20, color="seagreen",alpha = 0.7, edgecolor="black")

plt.xlabel('Age')

plt.ylabel('Frequency')

plt.title('Distribution of Age')

plt.grid(axis='y', linestyle='--')

plt.show()

# Plotting the distribution of BMI

plt.figure(figsize=(10, 6))

#3.2 Plotting the histogram with specified bins and color

plt.hist(patient_df['bmi'], bins=20, color="darkorange", alpha = 0.8,edgecolor="black")

plt.xlabel('BMI')

plt.ylabel('Frequency')

plt.title('Distribution of BMI')

plt.grid(axis='y', linestyle='--')

plt.show()

#3.3 Plotting the distribution of drinking alcohol

plt.figure(figsize=(10, 6))

# Plotting the histogram with specified bins and color

plt.hist(patient_df['alcohol_misuse'], bins=20, color="steelblue",alpha = 0.9, edgecolor="black")

plt.xlabel('Amount of Drinking Alcohol')

plt.ylabel('Frequency')

plt.title('Distribution of Drinking Alcohol')

plt.grid(axis='y', linestyle='--')

plt.show()

#3.4 Plotting the distribution of having mental health issues

plt.figure(figsize=(10, 6))

# Plotting the histogram with specified bins and color

plt.hist(patient_df['health_ment'], bins=20, color="brown",alpha = 0.7, edgecolor="black")

plt.xlabel('Mental Health')

plt.ylabel('Frequency')

plt.title('Distribution of Mental Health Issues')

plt.grid(axis='y', linestyle='--')

plt.show()

#3.5 Plotting the distribution of having general health

plt.figure(figsize=(10, 6))

# Plotting the histogram with specified bins and color

plt.hist(patient_df['health_gen'], bins=10, color="red", alpha=0.7, edgecolor="black")

plt.xlabel('General Health')

plt.ylabel('Frequency')

plt.title('Distribution of General Health')

plt.grid(axis='y', linestyle='--')

plt.show()

#3.6 Plotting the distribution of having healthy body

plt.figure(figsize=(10, 6))

# Plotting the histogram with specified bins, color, transparency, and edgecolor

plt.hist(patient_df['health_phys'], bins=15, color="purple", alpha=0.7, edgecolor="black")

plt.xlabel('Physical Health')

plt.ylabel('Frequency')

plt.title('Distribution of Physical Health')

plt.grid(axis='y', linestyle='--')

plt.show()