diff --git a/machine learning/Accuracy using SVM Model.png b/machine learning/Accuracy using SVM Model.png
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diff --git a/machine learning/Data Visualization.py b/machine learning/Data Visualization.py
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+++ b/machine learning/Data Visualization.py	
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+#Through this program, i am plotting the graph for comparison of year wise percentage of usage of bus.
+
+import numpy as np 
+import matplotlib.pyplot as plt  
+  
+   
+# creating the dataset 
+data = {'2015':71, '2016':81, '2017':84,  
+        '2018':79, '2019':85} 
+courses = list(data.keys()) 
+values = list(data.values()) 
+   
+fig = plt.figure(figsize = (10, 5)) 
+  
+# creating the bar plot 
+plt.bar(courses, values, color ='maroon',  
+        width = 0.4) 
+  
+plt.xlabel("Year wise Comparison") 
+plt.ylabel("Percentage Usage of Buses") 
+plt.title("Buses Usage Survey of 4 Years") 
+plt.show() 
diff --git a/machine learning/Face Original Image.png b/machine learning/Face Original Image.png
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diff --git a/machine learning/Face_Mask Result Image.png b/machine learning/Face_Mask Result Image.png
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diff --git a/machine learning/Face_Mask _Program.py b/machine learning/Face_Mask _Program.py
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+++ b/machine learning/Face_Mask _Program.py	
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+# -*- coding: utf-8 -*-
+"""FaceMask Program.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1nV51hyNEW5c_kN41Z_VruKXOlQMVR7TF
+
+Task - Face Mask Challange By Shubham Jha
+
+Problem - You are provided with some sample images along with this task. These are images extracted from a Celebrity Dataset to be used for segmentation. The task here though, is to crop the original image (named like 'xx.jpg') using it's corresponding mask provided (named like '000xx_skin.png').
+For a complete illustration see all cells below.
+
+For this task you should only use 'numpy' for the masking task. Please use standard image processing libraries like opencv, seaborn, matplotlib etc.
+
+Imports and Dependencies
+"""
+
+import numpy as np
+import pandas as pd
+import cv2 as cv 
+from google.colab.patches import cv2_imshow # for image display
+from skimage import io
+from PIL import Image 
+import matplotlib.pylab as plt
+import os
+from sklearn.datasets import load_sample_images
+
+from PIL import Image
+img = plt.imread('/content/FaceMaskData/62.jpg')
+plt.imshow(img)
+print(img.shape)
+
+img_face = cv2.imread('/content/FaceMaskData/62.jpg')
+img_mask = cv2.imread('/content/FaceMaskData/00062_skin.png')
+##Resizing images
+img_face = cv2.resize(img_face, (400,400), interpolation = cv2.INTER_AREA)
+img_mask = cv2.resize(img_mask, (400,400), interpolation = cv2.INTER_AREA)
+
+
+for h in range(len(img_mask)):
+    for w in range(len(img_mask)):
+        if img_mask[h][w][0] == 0:
+            for i in range(3):
+                img_face[h][w][i] = 0
+        else:
+            continue
+            
+plt.imshow(img_face)
+
+from PIL import Image
+img = plt.imread('/content/FaceMaskData/5.jpg')
+plt.imshow(img)
+print(img.shape)
+
+img_face = cv2.imread('/content/FaceMaskData/5.jpg')
+img_mask = cv2.imread('/content/FaceMaskData/00005_skin.png')
+##Resizing images
+img_face = cv2.resize(img_face, (400,400), interpolation = cv2.INTER_AREA)
+img_mask = cv2.resize(img_mask, (400,400), interpolation = cv2.INTER_AREA)
+
+for h in range(len(img_mask)):
+    for w in range(len(img_mask)):
+        if img_mask[h][w][0] == 0:
+            for i in range(3):
+                img_face[h][w][i] = 0
+        else:
+            continue
+            
+plt.imshow(img_face)
diff --git a/machine learning/Required Dataset_SvmAlgorithm.png b/machine learning/Required Dataset_SvmAlgorithm.png
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diff --git a/machine learning/SvmAlgorithm.py b/machine learning/SvmAlgorithm.py
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+++ b/machine learning/SvmAlgorithm.py	
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+"""Implementation of SVM Algorithm Using Python - By Shubham Jha
+"""
+
+from sklearn import svm, datasets
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.metrics import accuracy_score 
+from sklearn.model_selection import train_test_split
+
+iris = datasets.load_iris()
+
+X = iris.data[:, :12] # we will take twelve features
+y = iris.target
+x_train, x_test, y_train, y_test = train_test_split(X, y, random_state = 0, test_size = 0.25)
+
+clf = svm.SVC(kernel='linear', C=1).fit(x_train, y_train)
+
+import pandas as pd
+Iris = pd.read_csv("/content/train_qnU1GcL.csv")
+Iris.head(20)
+
+classifier_predictions = clf.predict(x_test)
+print(accuracy_score(y_test, classifier_predictions)*100)
+
+
+#Accuracy of this model is - 97.36842105263158