Machine-Learning/CaseStudyStep9.py at main · Omii3663/Machine-Learning · GitHub

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import pandas as pd

import matplotlib.pyplot as plt
import seaborn as sns

from sklearn.model_selection import train_test_split

from sklearn.tree import DecisionTreeClassifier

from sklearn.metrics import (
    accuracy_score,
    confusion_matrix,
    classification_report,
)


Border = "-"*30

##########################################
# Step1 : Load the Data Set
##########################################

print(Border)
print("Step1 : Load the DataSet")
print(Border)

DataPath = "iris.csv"

df = pd.read_csv(DataPath)

print("Dataset Loaded Succefully")

print("Initial enteries form dataset :")
print(df.head())
#print(df.tail()) -> for tail

##########################################
# Step2 : Data Analysis (EDA)
##########################################

print(Border)
print("Step2 : Data Analysis (EDA)")
print(Border)

print("Shape of dataset ",df.shape)

print("Column name :", list(df.columns))

print("Missing values per column : ")
print(df.isnull().sum())

print("Class distribution (species count)")
print(df["species"].value_counts())

print("Satatical report of Dataset :")
print(df.describe())

#####################################################
# Step3 : Decide independent & dependent variables
####################################################

print(Border)
print("Step3 : Decide independent & dependent variables")
print(Border)

# X : Independent Variable (features)
# Y : Dependent Variable (lables)

feature_col = [
    "sepal length (cm)",
    "sepal width (cm)",
    "petal length (cm)",
    "petal width (cm)"
               ]

X = df[feature_col]
Y = df["species"]

print("X Shape :",X.shape)
print("Y Shape :",Y.shape)

#####################################################
# Step4 : visualization of dataset
####################################################

print(Border)
print("Step4 : visualization of dataset")
print(Border)

#Scatter plot
plt.figure(figsize=(7,5))

for sp in df["species"].unique():
    temp = df[df["species"] == sp]
    plt.scatter(temp["petal length (cm)"],temp["petal width (cm)"],label = sp)

plt.title("Marvellous Iris Case study")

plt.xlabel("petal length (cm)")
plt.ylabel("petal width (cm)")

plt.legend()
plt.grid()
plt.show()

#####################################################
# Step5 : Split dataset for Traning & testing
####################################################

print(Border)
print("Step5 : Split dataset for Traning & testing")
print(Border)

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.5, random_state=42)

print("Data set splitting Activity Done ")

print("X :", X.shape)  #(150,4)
print("Y :", Y.shape) #(150,)

print("X_train :",X_train.shape) #(75,4)
print("X_test :",X_test.shape)  #(75,4)

print("Y_train :",Y_train.shape) #(75,)
print("Y_test :",Y_test.shape)  #(75,)

#####################################################
# Step6 : Bulid the model
####################################################

print(Border)
print("Step6 : Bulid the model")
print(Border)

model = DecisionTreeClassifier(max_depth=5)

print("Model gets Created succesfully")

#####################################################
# Step7 : Train the model
####################################################

print(Border)
print("Step7 : Train the model")
print(Border)

model.fit(X_train,Y_train)

print("Model tranaied sucssfully")

#####################################################
# Step8 : Test the model
####################################################

print(Border)
print("Step8 : Test the model")
print(Border)

Y_pred = model.predict(X_test)

print("Model testing done")

print("Expected Answer :")
print(Y_test)

print("Predicted answer :")
print(Y_pred)

#####################################################
# Step9 : Evaluate the model performance
####################################################

print(Border)
print("Step9 : Evaluate the model performance")
print(Border)

accuracy = accuracy_score(Y_test,Y_pred)
print("accuracy of model is :", accuracy*100)

print("Confustion matrix")
cm = confusion_matrix(Y_test,Y_pred)
print(cm)

print("Classification Report")
print(classification_report(Y_test,Y_pred))