diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 0000000..bbcbbe7
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: ''
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.
diff --git a/A_Star.py b/A_Star.py
new file mode 100644
index 0000000..582630f
--- /dev/null
+++ b/A_Star.py
@@ -0,0 +1,103 @@
+from queue import heappop, heappush
+from math import inf
+
+
+class Graph:
+    def __init__(self, directed=True):
+        self.edges = {}
+        self.huristics = {}
+        self.directed = directed
+
+    def add_edge(self, node1, node2, cost=1, __reversed=False):
+        try:
+            neighbors = self.edges[node1]
+        except KeyError:
+            neighbors = {}
+        neighbors[node2] = cost
+        self.edges[node1] = neighbors
+        if not self.directed and not __reversed:
+            self.add_edge(node2, node1, cost, True)
+
+    def set_huristics(self, huristics={}):
+        self.huristics = huristics
+
+    def neighbors(self, node):
+        try:
+            return self.edges[node]
+        except KeyError:
+            return []
+
+    def cost(self, node1, node2):
+        try:
+            return self.edges[node1][node2]
+        except:
+            return inf
+
+    def a_star_search(self, start, goal):
+        found, fringe, visited, came_from, cost_so_far = False, [
+            (self.huristics[start], start)], set([start]), {start: None}, {start: 0}
+        print('{:11s} | {}'.format('Expand Node', 'Fringe'))
+        print('--------------------')
+        print('{:11s} | {}'.format('-', str(fringe[0])))
+        while not found and len(fringe):
+            _, current = heappop(fringe)
+            print('{:11s}'.format(current), end=' | ')
+            if current == goal:
+                found = True
+                break
+            for node in self.neighbors(current):
+                new_cost = cost_so_far[current] + self.cost(current, node)
+                if node not in visited or cost_so_far[node] > new_cost:
+                    visited.add(node)
+                    came_from[node] = current
+                    cost_so_far[node] = new_cost
+                    heappush(fringe, (new_cost + self.huristics[node], node))
+            print(', '.join([str(n) for n in fringe]))
+        if found:
+            print()
+            return came_from, cost_so_far[goal]
+        else:
+            print('No path from {} to {}'.format(start, goal))
+            return None, inf
+
+    @staticmethod
+    def print_path(came_from, goal):
+        parent = came_from[goal]
+        if parent:
+            Graph.print_path(came_from, parent)
+        else:
+            print(goal, end='')
+            return
+        print(' =>', goal, end='')
+
+    def __str__(self):
+        return str(self.edges)
+
+
+graph = Graph(directed=True)
+graph.add_edge('A', 'B', 6)
+graph.add_edge('A', 'F', 3)
+graph.add_edge('B', 'D', 2)
+graph.add_edge('B', 'C', 5)
+graph.add_edge('C', 'E', 5)
+graph.add_edge('C', 'D', 1)
+graph.add_edge('D', 'E', 8)
+graph.add_edge('D', 'C', 1)
+graph.add_edge('E', 'I', 5)
+graph.add_edge('E', 'J', 5)
+graph.add_edge('I', 'G', 3)
+graph.add_edge('I', 'J', 3)
+graph.add_edge('I', 'H', 2)
+graph.add_edge('H', 'I', 2)
+graph.add_edge('H', 'F', 7)
+graph.add_edge('F', 'G', 5)
+graph.add_edge('F', 'A', 3)
+
+graph.set_huristics({'A': 10, 'B': 8, 'C': 5, 'D': 7,
+                    'E': 3, 'F': 6, 'G': 5, 'H': 3, 'I': 1, 'J': 0})
+start, goal = 'A', 'J'
+traced_path, cost = graph.a_star_search(start, goal)
+if (traced_path):
+    print('Path:', end=' ')
+    Graph.print_path(traced_path, goal)
+    print('\nCost:', cost)
diff --git a/Alpha_BetaPruning.py b/Alpha_BetaPruning.py
new file mode 100644
index 0000000..996cacf
--- /dev/null
+++ b/Alpha_BetaPruning.py
@@ -0,0 +1,30 @@
+def Alpha_Beta(D,Index,Turn,Score,A,B):
+    if D == 3:
+        return Score[Index]
+    if Turn:
+        Best = Beta
+        # Recur for left and right children
+        for i in range(0, 2):
+            V = Alpha_Beta(D+1,Index*2+i,False,Score,A,B)
+            Best = max(Best, V)
+            A= max(A,Best)
+            # Alpha Beta Pruning
+            if A>=B:
+                break
+        return Best
+    else:
+        Best = Alpha
+        # Recur for left and right children
+        for i in range(0, 2):
+            V = Alpha_Beta(D+1,Index*2+i,True,Score,A,B)
+            Best = min(Best,V)
+            B= min(B,Best)
+            # Alpha Beta Pruning
+            if A>=B:
+                break
+        return Best
+# Drive Code
+Alpha, Beta = 1000, -1000
+Scores = [3,5,6,9,1,2,0,-1]
+print("The optimal value is :", Alpha_Beta(0,0,True,Scores,Beta,Alpha))
+
diff --git a/BFS.py b/BFS.py
new file mode 100644
index 0000000..bbed486
--- /dev/null
+++ b/BFS.py
@@ -0,0 +1,21 @@
+graph={
+    'S' : ['A','C','D'],
+    'A' : [],
+    'B' : ['E'],
+    'C' : ['F'],
+    'D' : ['B','G'],
+    'E' : ['G'],
+    'F' : ['E','G'],
+    'G' : []
+}
+def bfs(graph,startNode):
+    queue,visited=[startNode],[]
+    while queue:
+        vertex=queue.pop(0)
+        for edge in graph[vertex]:
+            if edge not in visited:
+                visited.append(edge)
+                queue.append(edge)
+        return visited
+
+print(bfs(graph,'S'))
\ No newline at end of file
diff --git a/BFSANDFS-checkpoint.ipynb b/BFSANDFS-checkpoint.ipynb
deleted file mode 100644
index 3841be7..0000000
--- a/BFSANDFS-checkpoint.ipynb
+++ /dev/null
@@ -1,237 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Task 1\n",
-    "Using Depth First Search (DFS) recursive and iterative algorithm, write out the order in which nodes are added to the explored set, with start state S and goal state G. Break ties in alphabetical order. Additionally, what is the path returned by each algorithm? What is the total cost of each path?"
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MyGraph = {\n",
-    "    'S' : ['A','C','D'],\n",
-    "    'A' : [],\n",
-    "    'B' : ['E'],\n",
-    "    'C' : ['F'],\n",
-    "    'D' : ['B','G'],\n",
-    "    'E' : ['G'],\n",
-    "    'F' : ['E','G'],\n",
-    "    'G' : []\n",
-    "}\n",
-    "def DFS_Iterative_Alogorith(MyGraph,Start):\n",
-    "    Stack,visited[Start],[]\n",
-    "    while Stack:\n",
-    "        Vertex = Stack.pop()\n",
-    "        for N in MyGraph[Vertex]:\n",
-    "                 if Vertex not in visited:\n",
-    "                    visited.append(N)\n",
-    "                    Stack.append(N)\n",
-    "    return visited\n",
-    "print(DFS_Iterative_Alogorith(MyGraph,'S'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Recursive DFSMyGraph = {\n",
-    "MyGraph={\n",
-    "    'S' : ['A','C','D'],\n",
-    "    'A' : [],\n",
-    "    'B' : ['E'],\n",
-    "    'C' : ['F'],\n",
-    "    'D' : ['B','G'],\n",
-    "    'E' : ['G'],\n",
-    "    'F' : ['E','G'],\n",
-    "    'G' : []\n",
-    "}\n",
-    "def DFS_Recursive_Algorithm(MyGraph,Node,Visited):\n",
-    "    if Node not in Visited:\n",
-    "        Visited.append(Node)\n",
-    "        for N in MyGraph[Node]:\n",
-    "            DFS_Recursive_Algorithm(MyGraph,N,Visited)\n",
-    "    return Visited\n",
-    "print(DFS_Recursive_Algorithm(MyGraph,'S',[]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Task 2\n",
-    "Implement the Breadth First Search (BFS) both iterative and recursive algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MyGraph={\n",
-    "    'A' : ['B','C'],\n",
-    "    'B' : ['A','D','E'],\n",
-    "    'C' : ['A','F'],\n",
-    "    'D' : ['B'],\n",
-    "    'E' : ['B','F'],\n",
-    "    'F' : ['C','E']\n",
-    "}\n",
-    "def BFS_Iterative_Alogorith(MyGraph,root):\n",
-    "    Queue, visited = [root],[]\n",
-    "    while Queue:\n",
-    "        vertex = Queue.pop(0)\n",
-    "        for N in MyGraph[vertex]:\n",
-    "            if N not in visited:\n",
-    "                visited.append(N)\n",
-    "                Queue.append(N)\n",
-    "    return visited\n",
-    "print(BFS_Iterative_Alogorith(MyGraph,'A'))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MyGraph={\n",
-    "    'A' : ['B','C'],\n",
-    "    'B' : ['A','D','E'],\n",
-    "    'C' : ['A','F'],\n",
-    "    'D' : ['B'],\n",
-    "    'E' : ['B','F'],\n",
-    "    'F' : ['C','E']\n",
-    "}\n",
-    "def BFS_Recursive_Algorithm(MyGraph,Node,Visited):\n",
-    "    if Node not in Visited:\n",
-    "        Visited.append(Node)\n",
-    "        for N in MyGraph[Node]:\n",
-    "            BFS_Recursive_Algorithm(MyGraph,N,Visited)\n",
-    "    return Visited\n",
-    "print(BFS_Recursive_Algorithm(MyGraph,'A',[]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Task 3\n",
-    "Using Breadth First Search (BFS) recursive and iterative algorithm, write out the order in which nodes are added to the explored set, with start state S and goal state G. Break ties in alphabetical order. Additionally, what is the path returned by each algorithm? What is the total cost of each path?"
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "graph={\n",
-    "    'S' : ['A','C','D'],\n",
-    "    'A' : [],\n",
-    "    'B' : ['E'],\n",
-    "    'C' : ['F'],\n",
-    "    'D' : ['B','G'],\n",
-    "    'E' : ['G'],\n",
-    "    'F' : ['E','G'],\n",
-    "    'G' : []\n",
-    "}\n",
-    "def BFS_Iterative_Alogorith(graph,root):\n",
-    "    Queue, visited = [root],[]\n",
-    "    while Queue:\n",
-    "        vertex = Queue.pop(0)\n",
-    "        for N in graph[vertex]:\n",
-    "            if N not in visited:\n",
-    "                visited.append(N)\n",
-    "                Queue.append(N)\n",
-    "    return visited\n",
-    "print(BFS_Iterative_Alogorith(graph,'S'))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MyGraph={\n",
-    "    'S' : ['A','C','D'],\n",
-    "    'A' : [],\n",
-    "    'B' : ['E'],\n",
-    "    'C' : ['F'],\n",
-    "    'D' : ['B','G'],\n",
-    "    'E' : ['G'],\n",
-    "    'F' : ['E','G'],\n",
-    "    'G' : []\n",
-    "}\n",
-    "\n",
-    "def BFS_Recursive_Algorithm(MyGraph,Node,Visited):\n",
-    "    if Node not in Visited:\n",
-    "        Visited.append(Node)\n",
-    "        for N in MyGraph[Node]:\n",
-    "            BFS_Recursive_Algorithm(MyGraph,N,Visited)\n",
-    "    return Visited\n",
-    "print(BFS_Recursive_Algorithm(MyGraph,'S',[]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/DFS.py b/DFS.py
new file mode 100644
index 0000000..223bf63
--- /dev/null
+++ b/DFS.py
@@ -0,0 +1,21 @@
+graph={
+    'S' : ['A','C','D'],
+    'A' : [],
+    'B' : ['E'],
+    'C' : ['F'],
+    'D' : ['B','G'],
+    'E' : ['G'],
+    'F' : ['E','G'],
+    'G' : []
+}
+def dfs(graph,startNode):
+    stack,visited=[startNode],[]
+    while stack:
+        vertex=stack.pop(0)
+        for edge in graph[vertex]:
+            if edge not in visited:
+                visited.append(edge)
+                stack.append(edge)
+        return visited
+
+print(dfs(graph,'S'))
\ No newline at end of file
diff --git a/DataStructes-checkpoint.ipynb b/DataStructes-checkpoint.ipynb
deleted file mode 100644
index 617495f..0000000
--- a/DataStructes-checkpoint.ipynb
+++ /dev/null
@@ -1,486 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a stack called magazine, and  add bullets in it and after every bullet shot display the status of the magazine."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Magazine=list()\n",
-    "Magazine.append('.')\n",
-    "display(Magazine)\n",
-    "Magazine.append('.')\n",
-    "display(Magazine)\n",
-    "Magazine.append('.')\n",
-    "display(Magazine)\n",
-    "Magazine.append('.')\n",
-    "display(Magazine)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a queue and insert numbers in it and pop only that two number that have highest product."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "MyQueue=list()\n",
-    "MyQueue.append(1)\n",
-    "MyQueue.append(2)\n",
-    "MyQueue.append(3)\n",
-    "MyQueue.append(4)\n",
-    "\n",
-    "display(MyQueue)\n",
-    "product=MyQueue.pop(3)*MyQueue.pop(2)\n",
-    "print(product)\n",
-    "display(MyQueue)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a doubly linked list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Node(object):\n",
-    "    def __init__(self, data, Next = None, Previous = None):\n",
-    "        self.data = data\n",
-    "        self.next = Next\n",
-    "        self.previous = Previous\n",
-    "\n",
-    "    def getNext(self):\n",
-    "        return self.next\n",
-    "\n",
-    "    def getPrevious(self):\n",
-    "        return self.previous\n",
-    "\n",
-    "    def getData(self):\n",
-    "        return self.data\n",
-    "\n",
-    "    def setData(self, newData):\n",
-    "        self.data = newData\n",
-    "\n",
-    "    def setNext(self, newNext):\n",
-    "        self.next = newNext\n",
-    "\n",
-    "    def setPrevious(self, newPrevious):\n",
-    "        self.previous = newPrevious\n",
-    "\n",
-    "\n",
-    "class DoublyLinkedList(object):\n",
-    "    def __init__(self):\n",
-    "        self.head = None\n",
-    "\n",
-    "    def isEmpty(self):\n",
-    "        return self.head == None\n",
-    "\n",
-    "    def insertFirst(self, data):\n",
-    "        newNode = Node(data)\n",
-    "        if self.head:\n",
-    "            self.head.setPrevious(newNode)\n",
-    "        newNode.setNext(self.head)\n",
-    "        self.head = newNode\n",
-    "\n",
-    "    def insertLast(self, data):\n",
-    "        newNode = Node(data)\n",
-    "        current = self.head\n",
-    "        while current.getNext() != None:\n",
-    "            current = current.getNext()\n",
-    "        current.setNext(newNode)\n",
-    "        newNode.setPrevious(current)\n",
-    "\n",
-    "    def getAllData(self):\n",
-    "        ''' This function displays the data elements of the Linked List '''\n",
-    "        current = self.head\n",
-    "        elements = []\n",
-    "        while current:\n",
-    "            elements.append(current.getData())\n",
-    "            current = current.getNext()\n",
-    "\n",
-    "        return elements\n",
-    "\n",
-    "    def remove(self,item):\n",
-    "        current = self.head\n",
-    "        previous = None\n",
-    "        found = False\n",
-    "        while not found:\n",
-    "            if current.getData() == item:\n",
-    "                found = True\n",
-    "            else:\n",
-    "                previous = current\n",
-    "                current = current.getNext()\n",
-    "\n",
-    "        if previous == None:\n",
-    "            self.head = current.getNext()\n",
-    "        else:\n",
-    "            previous.setNext(current.getNext())\n",
-    "\n",
-    "if __name__ == '__main__':\n",
-    "    myList = DoublyLinkedList()\n",
-    "    myList.insertFirst(1)\n",
-    "    myList.insertFirst(12)\n",
-    "    myList.insertFirst(32)\n",
-    "    myList.insertFirst(22)\n",
-    "    myList.insertLast(2)\n",
-    "    myList.remove(12)\n",
-    "    print(myList.getAllData())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a tree and add values to it and after that traverse it in such a way that first you will print leafs and then thier parents and so on."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class Node:\n",
-    "    def __init__(self,Value):\n",
-    "        self.LeftChild=None\n",
-    "        self.Data=Value\n",
-    "        self.RightChild=None\n",
-    "class BinarySearchTree:\n",
-    "    def createNewNode(self,Data):\n",
-    "        return Node(Data)\n",
-    "    def InsertNode(self,Node,Data):\n",
-    "        if Node is None:\n",
-    "            return self.createNewNode(Data)\n",
-    "        if Data<Node.Data:\n",
-    "            Node.LeftChild=self.InsertNode(Node.LeftChild,Data)\n",
-    "        elif Data>Node.Data:\n",
-    "            Node.RightChild=self.InsertNode(Node.RightChild,Data)\n",
-    "        return Node\n",
-    "    def PostOrderTraversal(self,Root):\n",
-    "        if Root is not None:\n",
-    "            self.PostOrderTraversal(Root.LeftChild)\n",
-    "            self.PostOrderTraversal(Root.RightChild)\n",
-    "            print(Root.Data)\n",
-    "Root=None\n",
-    "BinarySearchTree=BinarySearchTree()\n",
-    "Root=BinarySearchTree.InsertNode(Root,10)\n",
-    "BinarySearchTree.InsertNode(Root,20)\n",
-    "BinarySearchTree.InsertNode(Root,30)\n",
-    "BinarySearchTree.InsertNode(Root,40)\n",
-    "BinarySearchTree.InsertNode(Root,50)\n",
-    "BinarySearchTree.InsertNode(Root,60)\n",
-    "BinarySearchTree.InsertNode(Root,70)\n",
-    "BinarySearchTree.PostOrderTraversal(Root)\n",
-    "\n",
-    "            "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a grapgh and check that which edge has a common vertices"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Graph={\"a\":[\"b,c\"],\n",
-    "       \"b\":[\"c,d\"],\n",
-    "       \"c\":[\"e,f\"],\n",
-    "       \"d\":[\"f,g\"],\n",
-    "       \"e\":[\"i,h\"]       \n",
-    "} \n",
-    "Edge=Graph.values()\n",
-    "print(Edge)\n",
-    "print(Graph)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a hash table and sort it according to its keys."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "HashTable={\"Name\":\"Hamza\",\"Age\":23,\"RegistrationNumber\":\"L1S18BSCS0123\"}\n",
-    "HashTable.update({\"Course\":\"DSA\"})\n",
-    "for i in sorted(HashTable.keys()):\n",
-    "    print(i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Design and implement a class named Car that have private attributes name, number of doors and fuel economy. You have to implement the default constructor along with setters getters. Interface will perform required tasks and display the information about two Cars.\n",
-    "\n",
-    "<b>Input:</b>\n",
-    "<br>\n",
-    "Name of the Car: Chevrolet<br>\n",
-    "Total Number of Doors: 4<br>\n",
-    "Fuel Economy of the Car (Km/Liter): 11.5<br>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class car: \n",
-    "    def __init__(self):\n",
-    "        self.__carname=\"BMW\"\n",
-    "        self.__numberofdoors=4\n",
-    "        self.__fueleconomy=22.5\n",
-    "    def set_carname(self,carname):\n",
-    "        self.__carname=carname\n",
-    "    def set_noofDoors(self,noofdoors):\n",
-    "        self.__numberofdoors=noofdoors\n",
-    "    def set_fuelEconomy(self,fuel_economy):\n",
-    "        self.__fueleconomy=fuel_economy\n",
-    "    def get_name(self):\n",
-    "        return self.__carname\n",
-    "    def get_numberofDoor(self):\n",
-    "        return self.__numberofdoors\n",
-    "    def get_fuelEconomy(self):\n",
-    "        return self.__fueleconomy\n",
-    "    def show(self):\n",
-    "        print(self.__carname)\n",
-    "        print(self.__numberofdoors)\n",
-    "        print(self.__fueleconomy)\n",
-    "car1=car()\n",
-    "car1.set_carname(\"RangeRover\")\n",
-    "print(car1.get_name())\n",
-    "car1.set_noofDoors(4)\n",
-    "print(car1.get_numberofDoor())\n",
-    "car1.set_fuelEconomy(12.5)\n",
-    "print(car1.get_fuelEconomy())\n",
-    "car2=car()\n",
-    "car2.set_carname(\"Toyota Corolla\")\n",
-    "print(car2.get_name())\n",
-    "car1.set_noofDoors(4)\n",
-    "print(car1.get_numberofDoor())\n",
-    "car1.set_fuelEconomy(13.5)\n",
-    "print(car1.get_fuelEconomy())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Design and implement a class named Student that have private attributes name, id, cell# and cgpa. You have to implement the default constructor along with setters getters. Interface will perform required tasks and display the information about three Students including their grade.\n",
-    "\n",
-    "<br><b>Note:</b> The grade will be awarded to students according to their final cgpa.\n",
-    "\n",
-    "<b>Input:</b>\n",
-    "<br>Name of the Student: Adeel Anwar\n",
-    "<br>Id of the Student: L1F12BSCS2099\n",
-    "<br>Cell# of the Student: 03009999999\n",
-    "<br>CGPA of the Student: 3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-2-9f5a88128c54>, line 38)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-2-9f5a88128c54>\"\u001b[1;36m, line \u001b[1;32m38\u001b[0m\n\u001b[1;33m    print(Student1.get_CGPA())\u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
-     ]
-    }
-   ],
-   "source": [
-    "class Student:\n",
-    "    def __init__(self):\n",
-    "        self.__StudentName=\"Inshal Naveed\" \n",
-    "        self.__StudentID=\"L1S18BSCS0156\"\n",
-    "        self.__StudentCell_no=\"03314828005\"\n",
-    "        self.__StudentCgpa=3.56\n",
-    "    def Set_studentName(self,Name):\n",
-    "        self.__StudentName=Name\n",
-    "    def Set_studentID(self,ID):\n",
-    "        self.__StudentID=ID\n",
-    "    def Set_StudentCellNo(self,CellNo):\n",
-    "        self.__StudentCell_no=CellNo\n",
-    "    def Student_CGPA(self,cgpa):\n",
-    "        self.__StudentCgpa=cgpa\n",
-    "    def get_studentName(self):\n",
-    "        return self.__StudentName\n",
-    "    def get_studentID(self):\n",
-    "        return self.__StudentID\n",
-    "    def get_Student_CellNo(self):\n",
-    "        return self.__StudentCell_no\n",
-    "    def get_CGPA(self):\n",
-    "        return self.__StudentCgpa\n",
-    "    def show(self):\n",
-    "        print(self.__StudentName)\n",
-    "        print(self.__StudentID)\n",
-    "        print(self.__StudentCell_no)\n",
-    "        print(self.__Student_Cgpa)\n",
-    "    \n",
-    "Student1=Student()\n",
-    "Student1.Set_studentName(\"Hamza Mansoor\")\n",
-    "print(Student1.get_studentName())\n",
-    "Student1.Set_studentID(\"L1S18BSCS0123\")\n",
-    "print(Student1.get_studentID())\n",
-    "Student1.Set_StudentCellNo(\"03314829005\")\n",
-    "print(Student1.get_Student_CellNo())\n",
-    "Student1.Student_CGPA(2.76)\n",
-    "print(\"Student1 grade is B-\")\n",
-    "print(Student1.get_CGPA())\n",
-    "Student2=Student()\n",
-    "Student2.Set_studentName(\"Inshal Naveed\")\n",
-    "print(Student2.get_studentName())\n",
-    "Student2.Set_studentID(\"L1S18BSCS0156\")\n",
-    "print(Student2.get_studentID())\n",
-    "Student2.Set_StudentCellNo(\"033148677921\")\n",
-    "print(Student2.get_Student_CellNo())\n",
-    "Student2.Student_CGPA(2.56)\n",
-    "print(\"Student2 Grade is C+\")\n",
-    "print(Student2.get_CGPA())\n",
-    "\n",
-    "Student3=Student()\n",
-    "Student3.Set_studentName(\"Osama Ayub\")\n",
-    "print(Student3.get_studentName())\n",
-    "Student3.Set_studentID(\"L1S18BSCS0095\")\n",
-    "print(Student3.get_studentID())\n",
-    "Student3.Set_StudentCellNo(\"03314828005\")\n",
-    "print(Student3.get_Student_CellNo())\n",
-    "Student3.Student_CGPA(2.30)\n",
-    "print(\"Student3 grade is C-\")\n",
-    "print(Student3.get_CGPA())\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next bridge software house Lahore requires an application that calculates employee’s increment on the given criteria. For the purpose software house wants his software engineer to design the application using object oriented approach. The class “NextBridge” is required to create with attributes and functions as mentioned below:\n",
-    "\n",
-    "<br>>name\n",
-    "<br>>address\n",
-    "<br>>phoneNumber \n",
-    "<br>>level//fresh,junior,senior\n",
-    "<br>>experience in years\n",
-    "\n",
-    "Implement the class having a parametrized constructor along with setters and getters. Also define a function that can calculate increment criteria based on the table below:\n",
-    "\n",
-    "<br>Experience&nbsp;&nbsp;&nbsp;&nbsp;Increment\n",
-    "<br>0&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;0%\n",
-    "<br>1-2&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;10%\n",
-    "<br>2-5&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;25%\n",
-    "<br>5 to onward&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;45%\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class NextBridge:\n",
-    "    def __init__(self,name,address,phoneNo,level,experience):\n",
-    "        self.__name=name\n",
-    "        self.__Address=address\n",
-    "        self.__PhoneNumber=phoneNo\n",
-    "        self.__Level=level\n",
-    "        self.__Experience=experience\n",
-    "    def Set_name(self,name):\n",
-    "        self.__name=name\n",
-    "    def get_name(self):\n",
-    "        return self.__name\n",
-    "    def show(self):\n",
-    "        print(self.__name)\n",
-    "        print(self.__Address)\n",
-    "        print(self.__PhoneNumber)\n",
-    "        print(self.__Level)\n",
-    "        print(self.__Experience)\n",
-    "    def set_Address(self,address):\n",
-    "        self.__Address=address\n",
-    "    def get_Address(self):\n",
-    "        return self.__Address\n",
-    "    def set_PhoneNo(self,phone_no):\n",
-    "        self.__PhoneNumber=phone_no\n",
-    "    def get_phone_No(self):\n",
-    "        return self.__PhoneNumber\n",
-    "    def set_level(self,level):\n",
-    "        self.__Level=level\n",
-    "    def get_level(self):\n",
-    "        return self.__Level\n",
-    "    def set_experience(self,exp):\n",
-    "        self.__Experience=exp\n",
-    "    def get_experience(self):\n",
-    "        return self.__Experience\n",
-    "    def Increment(self,experience):\n",
-    "        increment=experience*0.35*10\n",
-    "        print(increment)\n",
-    "        \n",
-    "NextBridge1=NextBridge('osama Ayub','170SD house Lane8 Askari 5','03314828005','Junior level',\"3 Year\")\n",
-    "NextBridge1.show()\n",
-    "print(\"increase in the Salary\\n\")\n",
-    "NextBridge1.Increment(3)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Dictonary.py b/Dictonary.py
new file mode 100644
index 0000000..b2e683a
--- /dev/null
+++ b/Dictonary.py
@@ -0,0 +1,107 @@
+# create a  Dictonary
+thisdic = {'Name' : 'osama' , 'Age' : 23 , 'year' : 1999}
+# print a Dictonary
+print(thisdic)
+
+# access the information in the Dictonary  with the help of the key
+print(thisdic['Name'])
+print(thisdic['year'])
+#acesskeynamethroughget()functionsDictonary
+y=thisdic.get("Name")
+print(y)
+
+#keys()function in the Dictonary
+x=thisdic.keys()
+print(x)
+#Values() function in the Dictonary
+y=thisdic.values()
+print(y)
+#items() function in the Dictonary
+x=thisdic.items()
+print(x)
+#check if the key is present or not in the Dictonary
+
+if "Name" in thisdic:
+    print("key is present in the Dictonary")
+if "color" not in thisdic:
+    print("key is not present in the Dictionary")
+#Add New item in the Dictionary
+thisdic["School"]="BeaconHouseSchoolSystem"
+print(thisdic)
+# duplicates are not allowed in  Dictonary   it will overwrite it
+thisdic['year'] = '2000'
+print(thisdic)
+#length of Dictionary
+print(len(thisdic))
+
+#TypeoftheDictonary
+print(type(thisdic))
+thisdic2={"Name": "osama" , "Age": 34 , "complex": 23j}
+print(thisdic2)
+# change the particular key's value
+thisdic['Name']='Hamza'
+print(thisdic)
+#update() the Dictionary items
+thisdic.update({"School": "CitySchool"})
+print(thisdic)
+thisdic.update({"Age" : 25})
+print(thisdic)
+#Add items in the Dictionary
+thisdic["Education"]="Bachelor of Computer Science"
+print(thisdic)
+thisdic["CourseName"]="Artificial_Intelligence"
+print(thisdic)
+thisdic.update({"color" : "blue"})
+print(thisdic)
+#Remove items from the Dictionary
+# pop() function
+thisdic.pop("CourseName")
+print(thisdic)
+# popitem()function
+thisdic.popitem()
+print(thisdic)
+# del function in dictionary
+del thisdic['year']
+del thisdic
+
+#clear() function in Dictionary
+thisdic2.clear()
+print(thisdic2)
+
+thisdic2={"Name": "osama" , "Age": 34 , "complex": 23j}
+for i in thisdic2:
+    print(i)
+for i in thisdic2:
+    print(thisdic2[i])
+for i in thisdic2.values():
+    print(i)
+
+
+for i in thisdic2.items():
+    print(i)
+for i in thisdic2.keys():
+    print(i)
+#copy Dictionary in python
+thisdic=thisdic2.copy()
+print(thisdic)
+
+#dict() for copying in the Dictionary
+thisdic=dict(thisdic2)
+print(thisdic)
+
+# Nested Dictionary in python
+myfamily = {
+  "child1" : {
+    "name" : "Emil",
+    "year" : 2004
+  },
+  "child2" : {
+    "name" : "Tobias",
+    "year" : 2007
+  },
+  "child3" : {
+    "name" : "Linus",
+    "year" : 2011
+  }
+}
+print(myfamily)
\ No newline at end of file
diff --git a/FileHandling-checkpoint.ipynb b/FileHandling-checkpoint.ipynb
deleted file mode 100644
index 4e43f1f..0000000
--- a/FileHandling-checkpoint.ipynb
+++ /dev/null
@@ -1,278 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Filing Tasks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a Python program to generate 26 text files according to the alphbetic order .e.g A.txt, B.txt etc?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "str=\"A B C D E F G H I J K L M N O P Q R S T U V W X Y Z\"\n",
-    "print(str.split(\" \"))\n",
-    "for i in (str): \n",
-    "    with open(i+\".txt\",\"w\") as file:\n",
-    "        file.write(str)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a Python program that takes a text file as input and returns the number of words of a given text file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename=input('enter a filename:')\n",
-    "file=open(filename,\"w\")\n",
-    "file.write(\"HelloWorld\")\n",
-    "file.close()\n",
-    "file=open(filename,\"r\")\n",
-    "x=len(filename)\n",
-    "print(\"words in a file\",x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a Python program that read a random line from a file that have text stored in the form of paragraph?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "filename=open(\"input.txt\")\n",
-    "\n",
-    "with open(\"input.txt\",\"r\")as lines:\n",
-    "    line=lines.readline()\n",
-    "    print(line)\n",
-    "    lines.close()\n",
-    "  \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a Python program that reads first line from first file and first line from second file and then combine that text and write it on third file?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename1=\" \"\n",
-    "filename2=\" \"\n",
-    "with open(\"input.txt\") as file1:\n",
-    "      filename1=file1.read()\n",
-    "file1.close()\n",
-    "\n",
-    "with open(\"output.txt\") as file2:\n",
-    "     filename2=file2.read()\n",
-    "file2.close()\n",
-    "merge=\"\\n\"\n",
-    "merge=filename1+filename2\n",
-    "with open(\"test.txt\",'w') as file3:\n",
-    "    file3.write(merge)\n",
-    "file3.close()\n",
-    "\n",
-    "       \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a Python program that reads a line from a text file and append that text to same file?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filename=open(\"input.txt\",\"r\")\n",
-    "print(filename.read())\n",
-    "filename.close()\n",
-    "filename=open(\"input.txt\",\"a\")\n",
-    "filename.close()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Write a Python program that check whether a file is closed or not?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "file=open(\"input.txt\")\n",
-    "if file.close==False:\n",
-    "    print(\"file is open\")\n",
-    "else:\n",
-    "    print(\"file is closed\")\n",
-    "    \n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Representation of Search Problem Tasks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 8 Puzzle Problem:\n",
-    "Here, we have a 3×3 matrix with movable tiles numbered from 1 to 8 with a blank space. The tile adjacent to the blank space can slide into that space. The objective is to reach a specified goal state similar to the goal state, as shown in the below figure."
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the figure, our task is to convert the current state into goal state by sliding digits into the blank space."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>The problem formulation:</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "IntialState:start with the matrix with the numbers 1 to 8 and a blank space.\n",
-    "Actions:We can only move blankspace up,down,left and right.\n",
-    "state space:we can apply dfs or bfs to solve the problem.\n",
-    "path:we can only make the blank_space move in four directions left,right,up and down but not diagonal.\n",
-    "GoalState/test:move the blank space to its correct place by sliding digits until the given goal state is reached.\n",
-    "solution:we will move the blank space left,right or up and down in different steps to reach the goal state.\n",
-    "TotalCost:cost to reach the goal state is 15 moves\n",
-    "pathcost: 1 per move\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>8-queens problem:</b><br>\n",
-    "The aim of this problem is to place eight queens on a chessboard in an order where no queen may attack another. A queen can attack other queens either diagonally or in same row and column. From the following figure, we can understand the problem as well as its correct solution."
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It is noticed from the above figure that each queen is set into the chessboard in a position where no other queen is placed diagonally, in same row or column. Therefore, it is one right approach to the 8-queens problem."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<b>The problem formulation:</b>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "IntialState: start with 0 queens on the chessboard\n",
-    "Actions:start placing the queen in the square of the chessboard\n",
-    "stateSpace:we need to place 8 queens on the chessboard not in same row,column and diagonal\n",
-    "path: we need to place only one queen in one row ,column and diagonal so they dont attack each other\n",
-    "GoalState/test:place 8 queens on chessboard sucessfully without each getting attacked\n",
-    "solution:We need to place 8 queens without attacking each other in the chessboard\n",
-    "TotalCost:11 per steps to place all the queens on  the chessboard\n",
-    "pathcost: 1 per move"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/FileHandling.py b/FileHandling.py
new file mode 100644
index 0000000..e5d4ade
--- /dev/null
+++ b/FileHandling.py
@@ -0,0 +1,33 @@
+
+# open a file in python using open() function
+import os
+f = open("input.txt")
+# read a text file
+f = open("input.txt", "rt")
+# read data from a file
+f = open("input.txt", "rt")
+print(f.read())
+# read  data into file up to 6 characters
+f = open("input.txt", "rt")
+print(f.read(6))
+# read one line from the file
+f = open("input.txt", "rt")
+print(f.readline())
+# Loop through the contents of a file
+for x in f:
+    print(x)
+# closing the files
+f = open("input.txt", "rt")
+print(f.read())
+f.close()
+# writing to an existing file using append
+f = open("input.txt", "a")
+f.write("Welcome To Python Programming Language")
+f.close()
+# overwrite the contents of a file using write
+f = open("input.txt", "w")
+f.write("Welcome to Python Programming Tutorial")
+f.close()
+# create a New file in python
+f = open("test.txt", "x")
+f.close()
diff --git a/ForLoops.py b/ForLoops.py
new file mode 100644
index 0000000..9a37394
--- /dev/null
+++ b/ForLoops.py
@@ -0,0 +1,47 @@
+List=["may","june"]
+for i in List:
+    print(i)
+
+# print a tuple
+
+tuple=("may","june")
+for i in tuple:
+    print(i)
+# print a string:
+x=["banana","Grape"]
+for i in "x":
+    print(i)
+# break statement
+
+for i in "x":
+    if i=="x":
+     break
+     print(i)
+# continue statement
+for i in "x":
+    if i=="x":
+     continue
+     print(i)
+# Range function
+for i in range(5):
+    print(i)
+# two ranges in for loop
+for i in range(2,5):
+    print(i)
+# 3 ranges in for loop
+for i in range(1,2,6):
+    print(i)
+# else in for loop
+
+for i in range(3):
+    print(i)
+else:
+    print("loop finished")
+# Nested for Loops
+number=1
+for i in range(1,10):
+
+    for j in range(0,i+1):
+        print(number,end=" ")
+        number=number+1
+    print("\r")
\ No newline at end of file
diff --git a/GBFS.py b/GBFS.py
new file mode 100644
index 0000000..224cc7a
--- /dev/null
+++ b/GBFS.py
@@ -0,0 +1,93 @@
+#Implement Greedy  Best Search
+from queue import heappop,heappush
+from math import inf
+class Graph:
+    def __init__(self, directed=True):
+        self.edges = {}
+        self.huristics = {}
+        self.directed = directed
+
+    def add_edge(self, node1, node2, cost=1, __reversed=False):
+        try:
+            neighbors = self.edges[node1]
+        except KeyError:
+            neighbors = {}
+        neighbors[node2] = cost
+        self.edges[node1] = neighbors
+        if not self.directed and not __reversed:
+            self.add_edge(node2, node1, cost, True)
+
+    def set_huristics(self, huristics={}):
+        self.huristics = huristics
+
+    def neighbors(self, node):
+        try:
+            return self.edges[node]
+        except KeyError:
+            return []
+
+    def cost(self, node1, node2):
+        try:
+            return self.edges[node1][node2]
+        except:
+            return inf
+
+    def best_first_search(self, start, goal):
+        found, fringe, visited, came_from, cost_so_far = False, [
+            (self.huristics[start], start)], set([start]), {start: None}, {start: 0}
+        print('{:11s} | {}'.format('Expand Node', 'Fringe'))
+        print('--------------------')
+        print('{:11s} | {}'.format('-', str(fringe[0])))
+        while not found and len(fringe):
+            _, current = heappop(fringe)
+            print('{:11s}'.format(current), end=' | ')
+            if current == goal:
+                found = True
+                break
+            for node in self.neighbors(current):
+                new_cost = cost_so_far[current] + self.cost(current, node)
+                if node not in visited or cost_so_far[node] > new_cost:
+                    visited.add(node)
+                    came_from[node] = current
+                    cost_so_far[node] = new_cost
+                    heappush(fringe, (new_cost + self.huristics[node], node))
+            print(', '.join([str(n) for n in fringe]))
+        if found:
+            print()
+            return came_from, cost_so_far[goal]
+        else:
+            print('No path from {} to {}'.format(start, goal))
+            return None, inf
+
+    @staticmethod
+    def print_path(came_from, goal):
+        parent = came_from[goal]
+        if parent:
+            Graph.print_path(came_from, parent)
+        else:
+            print(goal, end='')
+            return
+        print(' =>', goal, end='')
+
+    def __str__(self):
+        return str(self.edges)
+
+
+graph = Graph(directed=True)
+graph.add_edge('S', 'A', 3)
+graph.add_edge('S', 'B', 2)
+graph.add_edge('A', 'C', 4)
+graph.add_edge('A', 'D', 1)
+graph.add_edge('B', 'E', 3)
+graph.add_edge('B', 'F', 1)
+graph.add_edge('E', 'H', 5)
+graph.add_edge('F', 'I', 2)
+graph.add_edge('F', 'G', 3)
+graph.set_huristics({'A': 12, 'B': 4, 'C': 7, 'D': 3,
+                    'E': 8, 'F': 2, 'H': 4, 'I': 9, 'S': 13, 'G': 0})
+start, goal = 'S', 'G'
+traced_path, cost = graph.best_first_search(start, goal)
+if (traced_path):
+    print('Path:', end=' ')
+    Graph.print_path(traced_path, goal)
+    print('\nCost:', cost)
diff --git a/Genetic_Algorithm.py b/Genetic_Algorithm.py
new file mode 100644
index 0000000..e8a6c32
--- /dev/null
+++ b/Genetic_Algorithm.py
@@ -0,0 +1,109 @@
+import random
+
+def pick_rand(num):
+    
+    list2=['0','0','0','0','0']
+    list= bin(num).replace("b", "")
+    x=4
+    w=len(list)-1
+    for i in range(len(list)):
+        list2[x]=list[w]
+        w-=1
+        x-=1
+    return list2
+def fitness_of_num(num):
+    w=round((pow(-num,2)/10))
+    w=w*-1
+    w=w+3*num
+    return w
+def cross_over(list):
+    w=random.randint(1,4)
+    x=random.randint(1,4)
+    
+    if w>x:
+        temp=w
+        w=x
+        x=temp
+    for i in range(0,len(list),2):
+        for j in range(w,x):
+            temp=list[i][j]
+            list[i][j]=list[i+1][j]
+            list[i+1][j]=temp
+        
+            
+    return list
+def convert(list):
+    w=str(list)
+    x=''
+    for i in w:
+        if i=='0'or i=='1':
+            x+=i
+    return x
+
+def binaryToDecimal(binary): 
+
+    binary1 = binary 
+    decimal, i, n = 0, 0, 0
+    while(binary != 0): 
+        dec = binary % 10
+        decimal = decimal + dec * pow(2, i) 
+        binary = binary//10
+        i += 1
+    return decimal
+def check_fitness(fitness):
+    for i in range(len(fitness)):
+        if fitness[i]>=23:
+            return i
+    return -1
+def mutate(list):
+    w=random.randint(0,4)
+    x=random.randint(0,4)
+    for i in range(len(list)):
+        temp=list[i][w]
+        list[i][w]=list[i][x]
+        list[i][x]=temp
+    return list
+def askfitness(number):
+    fitness=[]
+    for i in range(len(number)):
+        fitness.append(fitness_of_num(number[i]))
+    return fitness
+    
+def convertonumbers(list):
+    number=[]
+    for i in range(len(list)):
+        w=convert(list[i])
+        number.append(binaryToDecimal(int(w)))
+
+    return number
+    
+    
+def main_function():
+    list=[]
+    number=[]
+    fitness=[]
+    
+    for x in range(10):
+        num=random.randint(0,31)
+        number.append(num)
+        list.append(pick_rand(num))
+        fitness.append(fitness_of_num(num))
+    w=check_fitness(fitness)   
+    if w!=-1:
+        print(list[w],"   ",fitness[w],"   ",number[w])
+    else:
+        num=1
+        while True:
+            list=cross_over(list)
+            number=convertonumbers(list)
+            fitness=askfitness(number)
+            w=check_fitness(fitness)
+            if w!=-1:
+                print(list[w]," The fitness of binary number is   ",fitness[w]," and the number is   ",number[w])
+                break
+            if num%3==0:
+                list=mutate(list)
+            num+=1
+main_function()
+
+    
\ No newline at end of file
diff --git a/HCS.py b/HCS.py
new file mode 100644
index 0000000..e1122fa
--- /dev/null
+++ b/HCS.py
@@ -0,0 +1,35 @@
+graph={'A':[(8,'B'),(20,'C')],
+       'B':[(25,'A'),(20,'C'),(6,'D')],
+       'C':[(25,'A'),(8,'B'),(12,'E')],
+       'D':[(8,'B'),(12,'E'),(0,'F')],
+       'E':[(20,'C'),(6,'D'),(0,'F')],
+       'F':[(6,'D'),(12,'E')]
+}
+def HillClimbing(graph,startNode,goalNode):
+    queue=[startNode]
+    visited=[]
+    success=[startNode]
+    while queue:
+         node=queue.pop(queue.index(min(queue)))
+         if node[1] not in visited:
+            size = len(success)-1
+            if node[0] <= success[size-1][0]:
+                visited.append(node[1])
+                success.append(node)
+            else:
+                print("Goal not found.")
+                return
+         if node[1] == goalNode:
+            return visited
+         for n in (graph[node[1]]):
+            queue.append(n)
+    return visited
+
+
+path=HillClimbing(graph,(25,'A'),'F')
+print("path is:",path)
+               
+               
+         
+     
+    
\ No newline at end of file
diff --git a/MinMaxAlogrithm.ipynb b/MinMaxAlogrithm.ipynb
deleted file mode 100644
index b28e411..0000000
--- a/MinMaxAlogrithm.ipynb
+++ /dev/null
@@ -1,176 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 1:\n",
-    "Find the winning value of the Maximizer (Node A).\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tree Depth is: 3\n",
-      "Winning Value of the Maximizier Node is: 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Implement Minimax Algorithm only for the winning value.\n",
-    "import math\n",
-    "def MinMaxAlgo(currentDepth,NodesIndex,Turns,Score,TargetDepth):\n",
-    "    \n",
-    "     if currentDepth==TargetDepth:\n",
-    "        return Score[NodesIndex]\n",
-    "     if(Turns):\n",
-    "        Max=max(MinMaxAlgo(currentDepth + 1,NodesIndex*2,False,Score,TargetDepth),MinMaxAlgo(currentDepth+1,NodesIndex*2+1,False,Score,TargetDepth))\n",
-    "        return Max\n",
-    "     else:\n",
-    "        Min=min(MinMaxAlgo(currentDepth+1,NodesIndex*2,True,Score,TargetDepth),MinMaxAlgo(currentDepth+1,NodesIndex*2+1,True,Score,TargetDepth))\n",
-    "        return Min\n",
-    "Terminal_values=[-1,4,2,6,-3,-5,0,7]\n",
-    "TreeDepth=int(math.log(len(Terminal_values),2))\n",
-    "print(\"Tree Depth is:\",TreeDepth)\n",
-    "print(\"Winning Value of the Maximizier Node is:\",end=\" \")\n",
-    "print(MinMaxAlgo(0,0,True,Terminal_values,TreeDepth))\n"
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 2:\n",
-    "Find the winning value of the maximizer along with the path.\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tree Depth is: 3\n",
-      "Winning Value of the Maximizier Node is: 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Implement Minimax Algorithm that print paths to wining/optimal value.\n",
-    "import math\n",
-    "def MinMaxAlgo(currentDepth,NodesIndex,Turns,Score,TargetDepth):\n",
-    "    \n",
-    "     if currentDepth==TargetDepth:\n",
-    "        return Score[NodesIndex]\n",
-    "     if(Turns):\n",
-    "        return max(MinMaxAlgo(currentDepth + 1,NodesIndex*2,False,Score,TargetDepth),MinMaxAlgo(currentDepth+1,NodesIndex*2+1,False,Score,TargetDepth))\n",
-    "     else:\n",
-    "        return min(MinMaxAlgo(currentDepth+1,NodesIndex*2,True,Score,TargetDepth),MinMaxAlgo(currentDepth+1,NodesIndex*2+1,True,Score,TargetDepth))\n",
-    "Terminal_values=[-1,3,5,1,-6,-4,0,9]\n",
-    "TreeDepth=int(math.log(len(Terminal_values),2))\n",
-    "print(\"Tree Depth is:\",TreeDepth)\n",
-    "print(\"Winning Value of the Maximizier Node is:\",end=\" \")\n",
-    "path=MinMaxAlgo(0,0,True,Terminal_values,TreeDepth)\n",
-    "print(path)\n"
-   ]
-  },
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Question 3:\n",
-    "Find the winning value of the maximizer along with the path.\n",
-    "- Green represents the Max\n",
-    "- Pink represents the Min\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tree Depth is: 4\n",
-      "Winning Value of the Maximizier Node is: 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "#Implement Minimax Algorithm that print paths to wining/optimal value.\n",
-    "import math\n",
-    "def MinMaxAlgo(currentDepth,NodesIndex,Turns,Score,TargetDepth):\n",
-    "    \n",
-    "     if currentDepth==TargetDepth:\n",
-    "        return Score[NodesIndex]\n",
-    "     if(Turns):\n",
-    "        return max(MinMaxAlgo(currentDepth + 1,NodesIndex*2,False,Score,TargetDepth),MinMaxAlgo(currentDepth+1,NodesIndex*2+1,False,Score,TargetDepth))\n",
-    "     else:\n",
-    "        return min(MinMaxAlgo(currentDepth+1,NodesIndex*2,True,Score,TargetDepth),MinMaxAlgo(currentDepth+1,NodesIndex*2+1,True,Score,TargetDepth))\n",
-    "Terminal_values=[5,7,1,-2,-8,-4,3,7,-9,2,3,1,-5,8,1,3,2]\n",
-    "TreeDepth=int(math.log(len(Terminal_values),2))\n",
-    "print(\"Tree Depth is:\",TreeDepth)\n",
-    "print(\"Winning Value of the Maximizier Node is:\",end=\" \")\n",
-    "path=MinMaxAlgo(0,0,True,Terminal_values,TreeDepth)\n",
-    "print(path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/Sets.py b/Sets.py
new file mode 100644
index 0000000..5e1f5d7
--- /dev/null
+++ b/Sets.py
@@ -0,0 +1,74 @@
+myset = {"May" , "June" , "July" , "August"}
+print(myset)
+
+print(len(myset))
+
+print(type(myset))
+
+for i in myset :
+    print(i)
+print("june" in myset)
+
+myset.add("September")
+print(myset)
+myset2 = {"osama" , " Abeera" , "Autumn"}
+# update items in set2 will be entered into set1
+myset.update(myset2)
+print(myset)
+
+list = [1 , 2 , 3 , 4 , 5]
+myset.update(list)
+print(myset)
+mytuple =("a" , "b" , "c" , "d")
+myset.update(mytuple)
+print(myset)
+myset.remove("May")
+print(myset)
+
+# remove selected element from the set
+myset.discard("a")
+print(myset)
+
+# pop out element out of the set
+myset.pop()
+print(myset)
+
+#clear the set elements
+myset.clear()
+print(myset)
+
+del myset
+
+#print a set through Loop
+for x in myset2:
+    print(x)
+
+# join two set union()
+myset={'summer' , 'winter' , 'Autumn', 'summer'}
+myset.union(myset2)
+print(myset)
+
+# it will only print duplicates
+myset.intersection(myset2)
+print(myset)
+# it will only print the duplicate items in the set
+myset.intersection_update(myset2)
+print(myset)
+# keep all the items and duplicates
+myset.symmetric_difference(myset2)
+print(myset)
+# keep all the items but not duplicates or not same
+myset.symmetric_difference_update(myset2)
+print(myset)
+myset.difference(myset2)
+print(myset)
+myset.difference_update(myset2)
+print(myset)
+myset={'osama'  , 'Autumn'  , 'Abeera'}
+myset.issubset(myset2)
+print(myset)
+myset.isdisjoint(myset2)
+print(myset)
+
+myset.issuperset(myset2)
+print(myset)
\ No newline at end of file
diff --git a/Strings.py b/Strings.py
new file mode 100644
index 0000000..d3f5a12
--- /dev/null
+++ b/Strings.py
@@ -0,0 +1 @@
+
diff --git a/conditional.py b/conditional.py
new file mode 100644
index 0000000..63a029a
--- /dev/null
+++ b/conditional.py
@@ -0,0 +1,26 @@
+
+x = 100
+y = 20
+z = 30
+w = 40
+
+if x<y:
+    print("x is less than y")
+elif x==y:
+    print("x is  than y")
+else:
+    print("x is greater than y")
+    #short Hand if
+if x>y:print("x is greater than y")
+#short hand if else
+print("x is less than y") if x<y else print("x is greater than y")
+# And operator condition if both true
+if x>y and z<w:
+    print("less")
+#or condition  if one true
+if x<y or w>z:
+    print("more")
+#Nested if statements
+#Pass statement
+if x>y:
+    pass
\ No newline at end of file
diff --git a/delete_file.py b/delete_file.py
new file mode 100644
index 0000000..8db0882
--- /dev/null
+++ b/delete_file.py
@@ -0,0 +1,6 @@
+import os
+
+if os.path.exists("input.txt"):
+    os.remove("input.txt")
+elif not os.path.exists("input.txt"):
+    print("file  does not exits")
diff --git a/devcontainer.json b/devcontainer.json
new file mode 100644
index 0000000..2eb62c4
--- /dev/null
+++ b/devcontainer.json
@@ -0,0 +1,50 @@
+{
+	"name": "GitHub Codespaces (Default)",
+	"build": {
+		"dockerfile": "Dockerfile"
+	},
+	"settings": {
+		"go.toolsManagement.checkForUpdates": "local",
+		"go.useLanguageServer": true,
+		"go.gopath": "/go",
+		"go.goroot": "/usr/local/go",
+		"python.pythonPath": "/opt/python/latest/bin/python",
+		"python.linting.enabled": true,
+		"python.linting.pylintEnabled": true,
+		"python.formatting.autopep8Path": "/usr/local/py-utils/bin/autopep8",
+		"python.formatting.blackPath": "/usr/local/py-utils/bin/black",
+		"python.formatting.yapfPath": "/usr/local/py-utils/bin/yapf",
+		"python.linting.banditPath": "/usr/local/py-utils/bin/bandit",
+		"python.linting.flake8Path": "/usr/local/py-utils/bin/flake8",
+		"python.linting.mypyPath": "/usr/local/py-utils/bin/mypy",
+		"python.linting.pycodestylePath": "/usr/local/py-utils/bin/pycodestyle",
+		"python.linting.pydocstylePath": "/usr/local/py-utils/bin/pydocstyle",
+		"python.linting.pylintPath": "/usr/local/py-utils/bin/pylint",
+		"lldb.executable": "/usr/bin/lldb",
+		"files.watcherExclude": {
+			"**/target/**": true
+		}
+	},
+	"remoteUser": "codespace",
+	"overrideCommand": false,
+	"mounts": ["source=codespaces-linux-var-lib-docker,target=/var/lib/docker,type=volume"],
+	"runArgs": [
+		"--cap-add=SYS_PTRACE",
+		"--security-opt",
+		"seccomp=unconfined",
+		"--privileged",
+		"--init"
+	],
+	
+	// Add the IDs of extensions you want installed when the container is created.
+	"extensions": [
+		"GitHub.vscode-pull-request-github"
+	],
+	
+	// Use 'forwardPorts' to make a list of ports inside the container available locally.
+	// "forwardPorts": [],
+
+	// "oryx build" will automatically install your dependencies and attempt to build your project
+	"postCreateCommand": "oryx build -p virtualenv_name=.venv --log-file /tmp/oryx-build.log --manifest-dir /tmp || echo 'Could not auto-build. Skipping.'"
+}
+© 2021 GitHub, Inc.
diff --git a/string_Formating.py b/string_Formating.py
new file mode 100644
index 0000000..750f3d4
--- /dev/null
+++ b/string_Formating.py
@@ -0,0 +1,32 @@
+# string Formating in python
+
+# format()
+
+string = "Hello Guys "
+text = " {} This is Python Programming"
+print(text.format(string))
+# format the price as Two decimals
+price = 49
+text = "The price is {:.2f} dollars"
+print(text.format(price))
+# Multiple Values format
+item = 43
+count = 10
+Number = 32
+text = " Count is {} and Item is {} and the Number is {}"
+print(text.format(item, count, Number))
+
+# format Index Numbers
+item = 34
+count = 23
+Number = 12
+text = "Count is {0} and Item is {1} and the Number is {2}"
+print(text.format(item, count, Number))
+# Index Number
+age = 24
+Name = "Osama"
+text = " His name is {1}. {1}  is {0} years old"
+print(text.format(age, Name))
+# Name Indexes
+myorder = "I have a car {carname},  and its  Model is {carmodel}."
+print(myorder.format(carname="Mustang GT ", carmodel=" 2019"))
diff --git a/tuple.py b/tuple.py
new file mode 100644
index 0000000..65e5541
--- /dev/null
+++ b/tuple.py
@@ -0,0 +1,56 @@
+mytuple = ("may" , "June" , "july")
+print(mytuple)
+
+mytuple2 = ("may" , "june" , "july" , "may")
+print(mytuple2)
+
+mytuple3 = (1 , 2 , 4 , 5)
+print(mytuple3)
+print(len(mytuple))
+mytuple4 = (True , True , False)
+print(mytuple4)
+print(type(mytuple4))
+
+mytuple5 =tuple(("osama" , "Abeera" , "hamza"))
+print(mytuple5)
+
+print(mytuple5[0:2])
+
+y = list(mytuple)
+print(y)
+y.append("August")
+mytuple = tuple(y)
+print(mytuple)
+# join two tuples
+mytuple6=mytuple4+mytuple5
+print(mytuple6)
+
+
+
+y = list(mytuple2)
+y.remove("may")
+mytuple4 =tuple(y)
+print(mytuple4)
+
+del mytuple3
+
+# unpacking of a tuple
+mytuple6 = ("apple" , "mango" , "grape")
+(green , *red, blue) = mytuple6
+print(green)
+print(blue)
+print(red)
+# iterate tuple  through  for loop
+
+mytuple3 = ("may" , "june" , "july" , "August" , "September")
+for i in range(len(mytuple3)):
+    print(mytuple3[i])
+    # iterate through while loop
+i = 0
+while i < len(mytuple3):
+    print(mytuple3[i])
+    i+=1
+
+# join two tuples
+mytuple7=mytuple3+mytuple
+print(mytuple7)
diff --git a/whileLoops.py b/whileLoops.py
new file mode 100644
index 0000000..56d1c97
--- /dev/null
+++ b/whileLoops.py
@@ -0,0 +1,7 @@
+i = 1
+# print multiplication of a number
+number = int(input("Enter a number"))
+while i <= number+1 :
+    table = number * i
+    print("table of a number is:" , table)
+    i+=1
