Quick sort

C++ algos/Sorting Algos/Mergesort.cpp

@@ -0,0 +1,68 @@
+#include<iostream>
+
+using namespace std;
+
+void  merge(int arr[],int l,int mid,int h)
+{
+ int b[5];
+ int i=l;
+ int k=l;
+ int j=mid+1;
+ while(i<=mid && j<=h)
+ {
+ if(arr[i]<arr[j])
+ {
+  b[k]=arr[i];
+  k++;
+  i++;
+  }
+  else
+  {
+   b[k]=arr[j];
+   k++;
+   j++;
+   }
+  }
+  while(i<=mid)
+  {
+   b[k]=arr[j];
+   k++;
+  }
+  while(j<=h)
+  {
+    b[k]=arr[i];
+    k++;
+   }
+ for(int k=0;k<=h;k++)
+ {
+  arr[i]=b[k];
+ }
+}
+
+void mergesort(int arr[],int l,int h)
+{
+ int mid=(l+h)/2;
+ mergesort(arr,l,mid);
+ mergesort(arr,(mid+1),h);
+merge(arr,l,mid,h);
+}
+
+int main()
+{
+ int myarr[],n;
+ cout<<"enter the number of elements"<<endl;
+ cin>>n;
+ cout<<"enter"<<n<<elements"<<endl;
+ for(int i=0;i<n;i++)
+ {
+  cin>>myarr[i];
+ }
+
+ mergesort(myarr,0,(n-1));
+
+for(int i=0;i<n;i++)
+{
+ cout<<myarr[i]<<endl;
+}
+return 0;
+}

C++ algos/Sorting Algos/Quicksort.cpp

@@ -0,0 +1,46 @@
+#include<iostream>
+#include<iomanip>
+
+using namespace std;
+
+int partition(int arr[],int s,int e)
+{
+ int pivot=arr[e];
+ int pindex=s;
+ for(int i=s;i<e;i++)
+ {
+  if(arr[i]<pivot)
+  {
+   arr[pindex]=arr[i];
+   pindex++;
+   }
+ }
+return pindex;
+}
+
+void quicksort(int arr,int s,int e)
+{
+ int p=partition(arr,s,e);
+ quicksort(arr,s,(p-1));
+ quicksort(arr,(p+1),e);
+}
+
+int main()
+{
+ int arr[],n;
+  cout<<"enter the number of elements to be sorted"<<endl;
+  cin>>n;
+ cout<<"enter"<<n<<"elements"<<endl;
+for(int i=0;i<n;i++)
+ {
+  cin>>arr[i];
+ }
+
+quicksort(arr,0,n);
+
+for(int i=0;i<n;i++)
+{
+ cout<<arr[i]<<endl;
+}
+return 0;
+}

Data Science Project/KNN.py

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+# importing libraries  
+import numpy as nm  
+import matplotlib.pyplot as mtp    
+import pandas as pd  
+
+#importing datasets  
+data_set= pd.read_csv('User_Data.csv')  
+
+#Extracting Independent and dependent Variable  
+x= data_set.iloc[:, [2,3]].values  
+y= data_set.iloc[:, 4].values  
+
+# Splitting the dataset into training and test set.  
+from sklearn.model_selection import train_test_split  
+x_train, x_test, y_train, y_test= train_test_split(x, y, test_size= 0.25, random_state=0)  
+
+#feature Scaling  
+from sklearn.preprocessing import StandardScaler    
+st_x= StandardScaler()    
+x_train= st_x.fit_transform(x_train)    
+x_test= st_x.transform(x_test) 
+
+#Fitting K-NN classifier to the training set  
+from sklearn.neighbors import KNeighborsClassifier  
+classifier= KNeighborsClassifier(n_neighbors=5, metric='minkowski',p=2)classifier.fit(x_train, y_train)
+
+
+#Predicting the test set result  
+y_pred= classifier.predict(x_test) 
+
+#Creating the Confusion matrix  
+from sklearn.metrics import confusion_matrix  
+cm= confusion_matrix(y_test, y_pred)  
+print("Confusion Matrix : \n",cm)
+
+
+#Visulaizing the trianing set result  
+from matplotlib.colors import ListedColormap  
+x_set, y_set = x_train, y_train  
+x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01),nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  
+mtp.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape),  
+alpha = 0.75, cmap = ListedColormap(('red','green' )))  
+mtp.xlim(x1.min(), x1.max())  
+mtp.ylim(x2.min(), x2.max())  
+for i, j in enumerate(nm.unique(y_set)):  
+    mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],  
+        c = ListedColormap(('red', 'green'))(i), label = j)  
+mtp.title('K-NN Algorithm (Training set)')  
+mtp.xlabel('Age')  
+mtp.ylabel('Estimated Salary')  
+mtp.legend()  
+mtp.show() 
+
+
+#Visualizing the test set result  
+from matplotlib.colors import ListedColormap  
+x_set, y_set = x_test, y_test  
+x1, x2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step  =0.01), nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01))  
+mtp.contourf(x1, x2, classifier.predict(nm.array([x1.ravel(), x2.ravel()]).T).reshape(x1.shape), alpha = 0.75, cmap = ListedColormap(('red','green' )))  
+mtp.xlim(x1.min(), x1.max())  
+mtp.ylim(x2.min(), x2.max())  
+for i, j in enumerate(nm.unique(y_set)):  
+    mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1],  
+        c = ListedColormap(('red', 'green'))(i), label = j)  
+mtp.title('K-NN algorithm(Test set)')  
+mtp.xlabel('Age')  
+mtp.ylabel('Estimated Salary')  
+mtp.legend()  
+mtp.show()  
+from sklearn.metrics import accuracy_score
+print ("Accuracy : ", accuracy_score(y_test, y_pred))