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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "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": "3.6.8"
+    },
+    "colab": {
+      "name": "201050_Tejas_part1",
+      "provenance": [],
+      "collapsed_sections": []
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rvFM645NE-D2"
+      },
+      "source": [
+        "# Assignment 1 - Part 1\n",
+        "In this assignment, we will go through basic linear algebra, NumPy, and image manipulation using Python to get everyone on the same page.\n",
+        "\n",
+        "One of the aims of this assignment is to get you to start getting comfortable searching for useful library functions online. So in many of the functions you will implement, you will have to look up helper functions.\n",
+        "\n",
+        "\\\n",
+        "\n",
+        "## Instructions\n",
+        "* This notebook contain blocks of code, you are required to complete those blocks(where required)\n",
+        "* You are required to copy this notebook (\"copy to drive\" above) and complete the code.\n",
+        "* For Submission, You'll be required to submit a sharable link for your copy of this notebook. (DO NOT CHANGE THE NAME OF THE FUNCTIONS)\n",
+        "\n",
+        "\\\n",
+        "\\\n",
+        "Also, I'd like to acknowledge the Stanford CS131. This assignment is highly based on the assignments from that course."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UhSVK4RoK9q5"
+      },
+      "source": [
+        "First Let's import some dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cCKqyfhIE-EQ"
+      },
+      "source": [
+        "# Imports the print function from newer versions of python\n",
+        "from __future__ import print_function\n",
+        "\n",
+        "# Setup\n",
+        "\n",
+        "# The Random module implements pseudo-random number generators\n",
+        "import random \n",
+        "\n",
+        "# Numpy is the main package for scientific computing with Python. \n",
+        "# This will be one of our most used libraries in this project\n",
+        "import numpy as np\n",
+        "\n",
+        "# The Time library helps us time code runtimes\n",
+        "import time\n",
+        "\n",
+        "\n",
+        "# Some more magic so that the notebook will reload external python modules;\n",
+        "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
+        "%load_ext autoreload\n",
+        "%autoreload 2\n",
+        "%reload_ext autoreload"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "QLtp15rqE-EU"
+      },
+      "source": [
+        "# Part 1: Linear Algebra and NumPy Review\n",
+        "In this section, we will review linear algebra and learn how to use vectors and matrices in python using numpy."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E8HDYpc0E-EV"
+      },
+      "source": [
+        "## Part 1.1 (5 points)\n",
+        "First, let's test whether you can define the following matrices and vectors using numpy. Look up `np.array()` for help. In the next code block, define $M$ as a $(4, 3)$ matrix, $a$ as a $(1, 3)$ row vector and $b$ as a $(3, 1)$ column vector:\n",
+        "\n",
+        "$$M = \\begin{bmatrix}\n",
+        "1 & 2 & 3 \\\\\n",
+        "4 & 5 & 6 \\\\\n",
+        "7 & 8 & 9 \\\\\n",
+        "10 & 11 & 12 \\end{bmatrix}\n",
+        "$$\n",
+        "\n",
+        "$$a = \\begin{bmatrix}\n",
+        "1 & 1 & 0\n",
+        "\\end{bmatrix}\n",
+        "$$\n",
+        "\n",
+        "$$b = \\begin{bmatrix}\n",
+        "-1 \\\\ 2 \\\\ 5\n",
+        "\\end{bmatrix}  \n",
+        "$$ "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mETk2NCME-EX",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d04f7792-4d53-46e8-968f-1e76faa87539"
+      },
+      "source": [
+        "### YOUR CODE HERE\n",
+        "M = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12]]).reshape(4,3)\n",
+        "a = np.array([1,1,0]).reshape(1,3)\n",
+        "b=np.array([[-1], [2], [5]]).reshape(3,1)\n",
+        "### END CODE HERE\n",
+        "print(\"M = \\n\", M)\n",
+        "print(\"The size of M is: \", np.size(M))\n",
+        "print()\n",
+        "print(\"a = \", a)\n",
+        "print(\"The size of a is: \", np.size(a))\n",
+        "print()\n",
+        "print(\"b = \", b)\n",
+        "print(\"The size of b is: \", np.size(b))"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "M = \n",
+            " [[ 1  2  3]\n",
+            " [ 4  5  6]\n",
+            " [ 7  8  9]\n",
+            " [10 11 12]]\n",
+            "The size of M is:  12\n",
+            "\n",
+            "a =  [[1 1 0]]\n",
+            "The size of a is:  3\n",
+            "\n",
+            "b =  [[-1]\n",
+            " [ 2]\n",
+            " [ 5]]\n",
+            "The size of b is:  3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rSta4NheE-EZ"
+      },
+      "source": [
+        "## Part 1.2 (5 points)\n",
+        "Implement the `dot_product()` method below and check that it returns the correct answer for $a^Tb$."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "C5ZRjCE2MVOU"
+      },
+      "source": [
+        "def dot_product(a, b):\n",
+        "    \"\"\"Implement dot product between the two vectors: a and b.\n",
+        "    (optional): While you can solve this using for loops, we recommend\n",
+        "    that you look up `np.dot()` online and use that instead.\n",
+        "    Args:\n",
+        "        a: numpy array of shape (x, n)\n",
+        "        b: numpy array of shape (n, x)\n",
+        "    Returns:\n",
+        "        out: numpy array of shape (x, x) (scalar if x = 1)\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    ### YOUR CODE HERE\n",
+        "    out= np.array(np.dot(a,b))\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pbLIS5vIE-Ea",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1df8eea3-e803-4d83-8289-d909684726ee"
+      },
+      "source": [
+        "\n",
+        "# Now, let's test out this dot product. Your answer should be [[1]].\n",
+        "aDotB = dot_product(a, b)\n",
+        "print(aDotB)\n",
+        "\n",
+        "print(\"The size is: \", aDotB.shape)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[1]]\n",
+            "The size is:  (1, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0rGfcRU1E-Eb"
+      },
+      "source": [
+        "## Part 1.3 (5 points)\n",
+        "Implement the `complicated_matrix_function()` method and use it to compute $(ab)Ma^T$\n",
+        "\n",
+        "IMPORTANT NOTE: The `complicated_matrix_function()` method expects all inputs to be two dimensional numpy arrays, as opposed to 1-D arrays.  This is an important distinction, because 2-D arrays can be transposed, while 1-D arrays cannot.\n",
+        "\n",
+        "To transpose a 2-D array, you can use the syntax `array.T` "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dglQmbuLNOk6"
+      },
+      "source": [
+        "def complicated_matrix_function(M, a, b):\n",
+        "    \"\"\"Implement (a * b) * (M * a.T).\n",
+        "    (optional): Use the `dot_product(a, b)` function you wrote above\n",
+        "    as a helper function.\n",
+        "    Args:\n",
+        "        M: numpy matrix of shape (x, n).\n",
+        "        a: numpy array of shape (1, n).\n",
+        "        b: numpy array of shape (n, 1).\n",
+        "    Returns:\n",
+        "        out: numpy matrix of shape (x, 1).\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    c=dot_product(a,b)\n",
+        "    d=dot_product(M,a.T)\n",
+        "    out=c* d\n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "da_uQQLhE-Ec",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0a0918fb-5563-4a10-a14d-e0c8857d4987"
+      },
+      "source": [
+        "# Your answer should be $[[3], [9], [15], [21]]$ of shape(4, 1).\n",
+        "ans = complicated_matrix_function(M, a, b)\n",
+        "print(ans)\n",
+        "print()\n",
+        "print(\"The size is: \", ans.shape)"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 3]\n",
+            " [ 9]\n",
+            " [15]\n",
+            " [21]]\n",
+            "\n",
+            "The size is:  (4, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6CWXxSSOE-Ed",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dd5a355a-20bc-4b96-99df-963ef65d4990"
+      },
+      "source": [
+        "M_2 = np.array(range(4)).reshape((2,2))\n",
+        "a_2 = np.array([[1,1]])\n",
+        "b_2 = np.array([[10, 10]]).T\n",
+        "print(M_2.shape)\n",
+        "print(a_2.shape)\n",
+        "print(b_2.shape)\n",
+        "print()\n",
+        "\n",
+        "# Your answer should be $[[20], [100]]$ of shape(2, 1).\n",
+        "ans = complicated_matrix_function(M_2, a_2, b_2)\n",
+        "print(ans)\n",
+        "print()\n",
+        "print(\"The size is: \", ans.shape)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(2, 2)\n",
+            "(1, 2)\n",
+            "(2, 1)\n",
+            "\n",
+            "[[ 20]\n",
+            " [100]]\n",
+            "\n",
+            "The size is:  (2, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4fHLxLl4E-Ee"
+      },
+      "source": [
+        "## Part 1.4 (10 points) [Optional/Bonus]\n",
+        "Implement `eigen_decomp()` and `get_eigen_values_and_vectors()` methods. In this method, perform eigenvalue decomposition on the following matrix and return the largest k eigen values and corresponding eigen vectors (k is specified in the method calls below).\n",
+        "\n",
+        "$$M = \\begin{bmatrix}\n",
+        "1 & 2 & 3 \\\\\n",
+        "4 & 5 & 6 \\\\\n",
+        "7 & 8 & 9 \\end{bmatrix}\n",
+        "$$\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "RfaCSoRMOIc8"
+      },
+      "source": [
+        "def eigen_decomp(M):\n",
+        "    \"\"\"Implement eigenvalue decomposition.\n",
+        "    (optional): You might find the `np.linalg.eig` function useful.\n",
+        "    Args:\n",
+        "        matrix: numpy matrix of shape (m, n)\n",
+        "    Returns:\n",
+        "        w: numpy array of shape (m, m) such that the column v[:,i] is the eigenvector corresponding to the eigenvalue w[i].\n",
+        "        v: Matrix where every column is an eigenvector.\n",
+        "    \"\"\"\n",
+        "    w = None\n",
+        "    v = None\n",
+        "    ### YOUR CODE HERE\n",
+        "    w,v= np.linalg.eig(M)\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "    return w, v"
+      ],
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YB120rb4ONBH"
+      },
+      "source": [
+        "def get_eigen_values_and_vectors(M, k):\n",
+        "    \"\"\"Return top k eigenvalues and eigenvectors of matrix M. By top k\n",
+        "    here we mean the eigenvalues with the top ABSOLUTE values (lookup\n",
+        "    np.argsort for a hint on how to do so.)\n",
+        "    (optional): Use the `eigen_decomp(M)` function you wrote above\n",
+        "    as a helper function\n",
+        "    Args:\n",
+        "        M: numpy matrix of shape (m, m).\n",
+        "        k: number of eigen values and respective vectors to return.\n",
+        "    Returns:\n",
+        "        eigenvalues: list of length k containing the top k eigenvalues\n",
+        "        eigenvectors: list of length k containing the top k eigenvectors\n",
+        "            of shape (m,)\n",
+        "    \"\"\"\n",
+        "    eigenvalues = []\n",
+        "    eigenvectors = []\n",
+        "    ### YOUR CODE HERE\n",
+        "    w,v=eigen_decomp(M)\n",
+        "    # L= np.concatenate(w,v, axis=0)\n",
+        "    #w stores the eigenvalues of M \n",
+        "    t=np.argsort(w) #this returns the indices which will sort the array of eigenvalues.\n",
+        "    count=0\n",
+        "    i=len(t)-1\n",
+        "    while count<k:\n",
+        "      eigenvalues.append(w[i])\n",
+        "      eigenvectors.append(v[:,i])\n",
+        "      i-=1\n",
+        "      count+=1\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "\n",
+        "    return eigenvalues, eigenvectors"
+      ],
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t0_GkrJwE-Ee",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "afe09b46-e832-4e41-ca2c-fe955df8ef99"
+      },
+      "source": [
+        " # Let's define M.\n",
+        "M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+        "w,v= np.linalg.eig(M)\n",
+        "print(w)\n",
+        "# t=(np.argsort(w))\n",
+        "# print(t)\n",
+        "# print(w)\n",
+        "# print(v)\n",
+        "# get_eigen_values_and_vectors(M, 1)\n",
+        "# Now let's grab the first eigenvalue and first eigenvector.\n",
+        "# You should get back a single eigenvalue and a single eigenvector.\n",
+        "\n",
+        "val, vec = get_eigen_values_and_vectors(M[:,:3], 1)\n",
+        "print(\"First eigenvalue =\", val[0])\n",
+        "print()\n",
+        "print(\"First eigenvector =\", vec[0])\n",
+        "# print(len(vec))\n",
+        "assert len(vec) == 1\n",
+        "\n",
+        "# # Now, let's get the first two eigenvalues and eigenvectors.\n",
+        "# # You should get back a list of two eigenvalues and a list of two eigenvector arrays.\n",
+        "val, vec = get_eigen_values_and_vectors(M[:,:3], 2)\n",
+        "print(\"Eigenvalues =\", val)\n",
+        "print()\n",
+        "print(\"Eigenvectors =\", vec)\n",
+        "assert len(vec) == 2"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[ 1.61168440e+01 -1.11684397e+00 -1.30367773e-15]\n",
+            "First eigenvalue = -1.3036777264747022e-15\n",
+            "\n",
+            "First eigenvector = [ 0.40824829 -0.81649658  0.40824829]\n",
+            "1\n",
+            "Eigenvalues = [-1.3036777264747022e-15, -1.1168439698070427]\n",
+            "\n",
+            "Eigenvectors = [array([ 0.40824829, -0.81649658,  0.40824829]), array([-0.78583024, -0.08675134,  0.61232756])]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Yeh-V5x1PYz5"
+      },
+      "source": [
+        "## Part 1.5 (10 points)\n",
+        "In this section, you'll implement a gaussian elimination.\n",
+        "\n",
+        "The algorithm to to reduce a matrix to rref using gaussian elimination contains 2 parts, First reducing the matrix to partial reduced form, then back substituting to calculate the rref. First algorithm can be summed up as:\n",
+        "1. Partial pivoting: Find the kth pivot by swapping rows, to move the entry with the largest absolute value to the pivot position. This imparts computational stability to the algorithm.\n",
+        "2. For each row below the pivot, calculate the factor f which makes the kth entry zero, and for every element in the row subtract the fth multiple of the corresponding element in the kth row.\n",
+        "3. Repeat above steps for each unknown. We will be left with a partial r.e.f. matrix.\n",
+        "\n",
+        "$$\\begin{bmatrix}\n",
+        "1 & 2 & 3 \\\\\n",
+        "4 & 5 & 6 \\\\\n",
+        "7 & 8 & 9 \\end{bmatrix}\n",
+        "=>\n",
+        "\\begin{bmatrix}\n",
+        "7 & 8 & 9 \\\\\n",
+        "4 & 5 & 6 \\\\\n",
+        "1 & 2 & 3 \\end{bmatrix}\n",
+        "=>\n",
+        "\\begin{bmatrix}\n",
+        "7 & 8 & 9 \\\\\n",
+        "0 & 0.42 & 0.85 \\\\\n",
+        "0 & 0.85 & 1.71 \\end{bmatrix}\n",
+        "=>\n",
+        "\\begin{bmatrix}\n",
+        "7 & 8 & 9 \\\\\n",
+        "0 & 0.85 & 1.71 \\\\\n",
+        "0 & 0.45 & 0.85 \\end{bmatrix}\n",
+        "=>\n",
+        "\\begin{bmatrix}\n",
+        "7 & 8 & 9 \\\\\n",
+        "0 & 0.42 & 0.85 \\\\\n",
+        "0 & 0 & -0.05 \\end{bmatrix}\n",
+        "$$\n",
+        "Second algorithm:\n",
+        "1. Take a pivot from the last row.\n",
+        "2. For each row above the pivot, calculate the factor f which makes the kth entry zero, and for every element in the row subtract the fth multiple of the corresponding element in the kth row\n",
+        "3. Repeat the above step untill the matrix is in rref\n",
+        "$$\\begin{bmatrix}\n",
+        "7 & 8 & 0 \\\\\n",
+        "0 & 0.42 & 0 \\\\\n",
+        "0 & 0 & -0.05 \\end{bmatrix}\n",
+        "=>\n",
+        "\\begin{bmatrix}\n",
+        "7 & 0 & 0 \\\\\n",
+        "0 & 0.42 & 0 \\\\\n",
+        "0 & 0 & -0.05 \\end{bmatrix}\n",
+        "$$\n",
+        "\n",
+        "Steps for implementation:\n",
+        "1. Complete the function `swap_rows()`\n",
+        "2. Complete the function `apply_row()`\n",
+        "3. Complete `forward()` and `backward()`\n",
+        "4. Finally implement `rref()` using the `forward()` and `backward()`\n",
+        "\n",
+        "Note: You can skip this part if you want."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qUFujiFAPYz6"
+      },
+      "source": [
+        "def swap_rows(M):\n",
+        "    \"\"\"Implement row swapping to make the largest element in the pivotial column to be the first row.\n",
+        "    Args:\n",
+        "        matrix: numpy matrix of shape (m, n)\n",
+        "    Returns:\n",
+        "        Ms: matrix with swapped row\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    Ms=M\n",
+        "    flag=0\n",
+        "    for i in range(0,np.shape(M)[1]):\n",
+        "      if(np.max(abs(M[:,i]))!=0): #finds the first non zero column\n",
+        "          c=(np.max(M[:,i])) #finds the maximum in it\n",
+        "          col=i  #col is the index of the pivotal column \n",
+        "          flag=1\n",
+        "          break\n",
+        "    if(flag==1):\n",
+        "      for i in range(np.shape(M)[0]):\n",
+        "        if(M[i][col]==c):\n",
+        "          t= Ms[0,:].copy() \n",
+        "          Ms[0,:]=Ms[i,:]\n",
+        "          Ms[i,:]=t #swaps the topmost row and the row with c. \n",
+        "    ### END YOUR CODE\n",
+        "    return Ms"
+      ],
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S8lbAUSWWpyO"
+      },
+      "source": [
+        "def apply_rows(M):\n",
+        "    \"\"\"For each row below the pivot, calculate the factor f which makes the kth\n",
+        "    entry zero, and for every element in the row subtract the fth multiple of the\n",
+        "    corresponding element in the kth row.\n",
+        "    Args:\n",
+        "        matrix: numpy matrix of shape (m, n)\n",
+        "    Returns:\n",
+        "        Ms: matrix with all other entries of the pivotal col zero\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    Ms= np.around(M.astype(float),3)\n",
+        "    ### YOUR CODE HERE\n",
+        "    for i in range(1,np.shape(Ms)[0]): \n",
+        "      f= Ms[i,0]/Ms[0,0]\n",
+        "      # print(f)\n",
+        "      Ms[i,:]= Ms[i,:]- (Ms[i,0]*Ms[0,:])/Ms[0,0]\n",
+        "      # print(Ms[i,:])\n",
+        "      Ms= np.around(Ms,3)\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "    return Ms"
+      ],
+      "execution_count": 70,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GnE_-JLxPYz7"
+      },
+      "source": [
+        "def forward(M):\n",
+        "    \"\"\"Return a partial ref using the algo described above\n",
+        "    Args:\n",
+        "        M: numpy matrix of shape (m, n).\n",
+        "    Returns:\n",
+        "        Ms: ref of M\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    Ms= np.around(M.astype(float),3)\n",
+        "    T=np.around(M.astype(float),3)\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    for i in range(0,np.shape(M)[0]-1):\n",
+        "      Ms=T[i:np.shape(M)[0], i:np.shape(M)[1]] #at i=0, this is the whole matrix \n",
+        "      # print(\"Ms before algo in the \", i, \"th iteration is : \", Ms)\n",
+        "      Ms=backward(Ms)\n",
+        "      # print(\"Ms in the \", i, \"th iteration is : \", Ms)\n",
+        "      T[i:np.shape(M)[0], i:np.shape(M)[1]]=Ms\n",
+        "    ### END YOUR CODE\n",
+        "    return T"
+      ],
+      "execution_count": 71,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Wb7pPGP4XmJu"
+      },
+      "source": [
+        "def backward(M):\n",
+        "    \"\"\"Return a rref using the algo described above\n",
+        "    Args:\n",
+        "        M: numpy matrix of shape (m, n).\n",
+        "    Returns:\n",
+        "        Ms: rref of M\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    Ms=M\n",
+        "    #This does bckward prop for lowest row/pivot\n",
+        "    ### YOUR CODE HERE\n",
+        "    r= np.shape(Ms)[0]-1\n",
+        "    c= np.shape(Ms)[1]-1\n",
+        "    for i in (range(0, r)):\n",
+        "      f= Ms[i,c]/Ms[r,c]\n",
+        "      Ms[i,:]= Ms[i,:]- (Ms[c,:]*f)\n",
+        "      Ms= np.around(Ms,3)\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "    return Ms\n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 82,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "XLq81xzXYR85"
+      },
+      "source": [
+        "def rref(M):\n",
+        "    \"\"\"Return a rref using the algo descrbed above\n",
+        "    Args:\n",
+        "        M: numpy matrix of shape (m, n).\n",
+        "    Returns:\n",
+        "        Ms: ref of M\n",
+        "    \"\"\"\n",
+        "    out = None\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    Ms= forward(M)\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    r= np.shape(Ms)[0]\n",
+        "    c= np.shape(Ms)[1]\n",
+        "    for i in range(0,np.shape(M)[0]-1):\n",
+        "      Ms=T[0:r-i, 0:c-i]\n",
+        "      # print(\"Ms before algo in the \", i, \"th iteration is : \", Ms)\n",
+        "      Ms= backward(Ms)\n",
+        "      # print(\"Ms in the \", i, \"th iteration is : \", Ms)\n",
+        "      T[0:r-i, 0:c-i]=Ms\n",
+        "      # print(\"T in the\", i,\" th iteration is : \" ,T)\n",
+        "    ### END YOUR CODE\n",
+        "    return T"
+      ],
+      "execution_count": 90,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Eiz6EbsWPYz8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "2108d9b5-6017-4dc3-d523-583491bd0d4b"
+      },
+      "source": [
+        "# Let's define M.\n",
+        "M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+        "# Now let's calculate it's rref.\n",
+        "# Note that your code may be evaluated on other test cases as well\n",
+        "Mrref = rref(M)\n",
+        "print(Mrref)\n"
+      ],
+      "execution_count": 91,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[ 7.    0.    0.  ]\n",
+            " [ 0.    0.42  0.  ]\n",
+            " [ 0.    0.   -0.05]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G46pyDzAE-Ef"
+      },
+      "source": [
+        "## Part 1.6 (10 points)\n",
+        "\n",
+        "To wrap up our overview of NumPy, let's implement something fun &mdash; a helper function for computing the Euclidean distance between two $n$-dimensional points!\n",
+        "\n",
+        "In the 2-dimensional case, computing the Euclidean distance reduces to solving the Pythagorean theorem $c = \\sqrt{a^2 + b^2}$. where, given two points $(x_1, y_1)$ and $(x_2, y_2)$, $a = x_1 - x_2$ and $b = y_1 - y_2$.\n",
+        "\n",
+        "\n",
+        "More generally, given two $n$-dimensional vectors, the Euclidean distance can be computed by:\n",
+        "\n",
+        "1. Performing an elementwise subtraction between the two vectors, to get $n$ difference values.\n",
+        "2. Squaring each of the $n$ difference values, and summing the squares.\n",
+        "4. Taking the square root of our sum.\n",
+        "\n",
+        "Alternatively, the Euclidean distance between length-$n$ vectors $u$ and $v$ can be written as:\n",
+        "\n",
+        "$\n",
+        "\\quad\\textbf{distance}(u, v) = \\sqrt{\\sum_{i=1}^n (u_i - v_i)^2}\n",
+        "$\n",
+        "\n",
+        "\n",
+        "Try implementing this function: first using native Python with a `for` loop in the `euclidean_distance_native()` function, then in NumPy **without any loops** in the `euclidean_distance_numpy()` function.\n",
+        "We've added some `assert`  statements here to help you check functionality (if it prints nothing, then your implementation is correct)!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5xvHopPqO29C"
+      },
+      "source": [
+        "def euclidean_distance_native(u, v):\n",
+        "    \"\"\"Computes the Euclidean distance between two vectors, represented as Python\n",
+        "    lists.\n",
+        "    Args:\n",
+        "        u (List[float]): A vector, represented as a list of floats.\n",
+        "        v (List[float]): A vector, represented as a list of floats.\n",
+        "    Returns:\n",
+        "        float: Euclidean distance between `u` and `v`.\n",
+        "    \"\"\"\n",
+        "    # First, run some checks:\n",
+        "    assert isinstance(u, list)\n",
+        "    assert isinstance(v, list)\n",
+        "    assert len(u) == len(v)\n",
+        "    sum=0\n",
+        "    for i in range(0,len(u)):\n",
+        "        sum+=((u[i]-v[i])**2)\n",
+        "        # print(i)\n",
+        "    # Compute the distance!\n",
+        "    # Notes:\n",
+        "    #  1) Try breaking this problem down: first, we want to get\n",
+        "    #     the difference between corresponding elements in our\n",
+        "    #     input arrays. Then, we want to square these differences.\n",
+        "    #     Finally, we want to sum the squares and square root the\n",
+        "    #     sum.\n",
+        "    out = np.sqrt(sum)\n",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 33,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wvLuK8MuO3LH"
+      },
+      "source": [
+        "def euclidean_distance_numpy(u, v):\n",
+        "    \"\"\"Computes the Euclidean distance between two vectors, represented as NumPy\n",
+        "    arrays.\n",
+        "    Args:\n",
+        "        u (np.ndarray): A vector, represented as a NumPy array.\n",
+        "        v (np.ndarray): A vector, represented as a NumPy array.\n",
+        "    Returns:\n",
+        "        float: Euclidean distance between `u` and `v`.\n",
+        "    \"\"\"\n",
+        "    # First, run some checks:\n",
+        "    assert isinstance(u, np.ndarray)\n",
+        "    assert isinstance(v, np.ndarray)\n",
+        "    assert u.shape == v.shape\n",
+        "    x= u-v \n",
+        "    out=np.dot(x.T,x)\n",
+        "    out= np.sqrt(out)  \n",
+        "    # Compute the distance!\n",
+        "    # Note:\n",
+        "    #  1) You shouldn't need any loops\n",
+        "    #  2) Some functions you can Google that might be useful:\n",
+        "    #         np.sqrt(), np.sum()\n",
+        "    #  3) Try breaking this problem down: first, we want to get\n",
+        "    #     the difference between corresponding elements in our\n",
+        "    #     input arrays. Then, we want to square these differences.\n",
+        "    #     Finally, we want to sum the squares and square root the\n",
+        "    #     sum.\n",
+        "\n",
+        "    ### YOUR CODE HERE\n",
+        "    return out \n",
+        "    pass\n",
+        "    ### END YOUR CODE"
+      ],
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wu9MimVJE-Eg"
+      },
+      "source": [
+        "## Testing native Python function\n",
+        "assert euclidean_distance_native([7.0], [6.0]) == 1.0\n",
+        "assert euclidean_distance_native([7.0, 0.0], [3.0, 3.0]) == 5.0\n",
+        "assert euclidean_distance_native([7.0, 0.0, 0.0], [3.0, 0.0, 3.0]) == 5.0"
+      ],
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kJDk88g1E-Ej"
+      },
+      "source": [
+        "## Testing NumPy function\n",
+        "assert euclidean_distance_numpy(\n",
+        "    np.array([7.0]),\n",
+        "    np.array([6.0])\n",
+        ") == 1.0\n",
+        "assert euclidean_distance_numpy(\n",
+        "    np.array([7.0, 0.0]),\n",
+        "    np.array([3.0, 3.0])\n",
+        ") == 5.0\n",
+        "assert euclidean_distance_numpy(\n",
+        "    np.array([7.0, 0.0, 0.0]),\n",
+        "    np.array([3.0, 0.0, 3.0])\n",
+        ") == 5.0"
+      ],
+      "execution_count": 36,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "n = 1000\n",
+        "\n",
+        "# Create some length-n lists and/or n-dimensional arrays\n",
+        "a = [0.0] * n\n",
+        "b = [10.0] * n\n",
+        "a_array = np.array(a)\n",
+        "b_array = np.array(b)\n",
+        "\n",
+        "# Compute runtime for native implementation\n",
+        "start_time = time.time()\n",
+        "for i in range(10000):\n",
+        "    euclidean_distance_native(a, b)\n",
+        "print(\"Native:\", (time.time() - start_time), \"seconds\")\n",
+        "\n",
+        "# Compute runtime for numpy implementation\n",
+        "# Start by grabbing the current time in seconds\n",
+        "start_time = time.time()\n",
+        "for i in range(10000):\n",
+        "    euclidean_distance_numpy(a_array, b_array)\n",
+        "print(\"NumPy:\", (time.time() - start_time), \"seconds\")"
+      ],
+      "metadata": {
+        "id": "E7Z38WwHhoNl",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5aa04300-d529-4ac6-8f15-cc57bbc86980"
+      },
+      "execution_count": 37,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Native: 1.5537786483764648 seconds\n",
+            "NumPy: 0.06700706481933594 seconds\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Mjik4mQXE-Ek"
+      },
+      "source": [
+        "Next, let's take a look at how these two implementations compare in terms of runtime:"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t4e6MfhHE-Em"
+      },
+      "source": [
+        "As you can see, doing vectorized calculations (i.e. no for loops) with NumPy results in significantly faster computations! "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Congrats You've come to the end of this notebook. If you solved everything above, impressive. If not, you might need to read/think a bit more. You can always ask doubts. Also, Note that you should submit it even if you cannot solve everything. We might evaluate these using a script later."
+      ],
+      "metadata": {
+        "id": "XvFE0Q5bhx6-"
+      }
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment/Assignment_1/201050_Tejas_part2.ipynb b/Assignment/Assignment_1/201050_Tejas_part2.ipynb
new file mode 100644
index 0000000..29c06fa
--- /dev/null
+++ b/Assignment/Assignment_1/201050_Tejas_part2.ipynb
@@ -0,0 +1,551 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "201050_Tejas_part2.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "JsM9yumHP9iu"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np \n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "\n",
+        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Stamatics /House_prediction.csv')\n",
+        "# print (df.head(2))\n",
+        "df.loc[df['floor']=='-', \"floor\"]=0\n",
+        "df.loc[df['furniture']=='furnished', \"furniture\"]=1\n",
+        "df.loc[df['furniture']=='not furnished', \"furniture\"]=0\n",
+        "df['floor'] = df['floor'].astype(int)\n",
+        "df['furniture']= df['furniture'].astype(int)\n",
+        "# print (df.head(2))\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Try to find a dependence of House Association Tax, property tax, and fire insurance on other features.\n",
+        "Not a formula but something intuitive like if it is increasing/decreasing with no of rooms or being furnished or not.# New Section"
+      ],
+      "metadata": {
+        "id": "GE0gbTHVD7lt"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "plt.figure(1)\n",
+        "mean_rent= df.groupby(['city'])['rent amount (R$)'].mean().plot(color = 'green',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_hoa= df.groupby(['city'])['hoa (R$)'].mean().plot(color = 'red',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "\n",
+        "plt.figure(2)\n",
+        "mean_property_tax= df.groupby(['city'])['property tax (R$)'].mean().plot(color = 'blue',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "\n",
+        "plt.figure(3)\n",
+        "mean_fire_insurance= df.groupby(['city'])['fire insurance (R$)'].mean().plot(color = 'red',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "\n",
+        "\n",
+        "# print(plt.ylim())\n",
+        "plt.figure(4)\n",
+        "mean_rooms= df.groupby(['city'])['rooms'].mean().plot(color = 'red',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_floor= df.groupby(['city'])['floor'].mean().plot(color = 'brown',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_floor= df.groupby(['city'])['parking spaces'].mean().plot(color = 'green',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "# parking spaces\n",
+        "\n",
+        "plt.figure(5)\n",
+        "mean_area= df.groupby(['city'])['area'].mean().plot(color = 'blue',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "\n",
+        "plt.figure(6)\n",
+        "mean_total_rent= df.groupby(['city'])['total (R$)'].mean().plot(color = 'purple',\n",
+        "         linestyle = 'solid', marker = 'o',legend=True)\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "UCuFbuVdECfb",
+        "outputId": "19129155-31a9-434d-cae2-6fccbbaf7e04"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Porto Alegre has the least average area, rooms, parking spaces, and rent. Campinas has relatively higher area but lower property tax and rent. "
+      ],
+      "metadata": {
+        "id": "6nnM3uMAyrql"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "sfRnLgF7yrYj"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "0ubGUhtizh9_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#dependancies \n",
+        "#maybe try to find a dependency of room rent on number of rooms? \n",
+        "#can plot the mean of rent \n",
+        "plt.figure()\n",
+        "plt.title('Variation of rent with number of rooms')\n",
+        "mean_rooms= df.groupby(['rooms'])['rent amount (R$)'].mean().plot.bar()\n",
+        "plt.figure(figsize=(11,15))\n",
+        "plt.title('Variation of rent with number of floors')\n",
+        "plt.xlim([0, 51])\n",
+        "# mean_rooms= df.groupby(['floor'])['rent amount (R$)'].mean().plot(color = 'red',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_rooms= df.groupby(['floor'])['rent amount (R$)'].mean().plot.bar()\n",
+        "\n",
+        "area_col_rounded= np.round(df['area'], -2)\n",
+        "area_col=df['area']\n",
+        "df['area']= area_col_rounded\n",
+        "plt.figure(figsize=(16,15))\n",
+        "plt.title('Variation of rent with area')\n",
+        "plt.xlim([0, 1200])\n",
+        "# mean_rooms= df.groupby(['area'])['rent amount (R$)'].mean().plot(color = 'red',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_rooms= df.groupby(['area'])['rent amount (R$)'].mean().plot.bar(color ='green',\n",
+        "        edgecolor ='yellow', label ='Area')\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.title('Variation of rent on whether apartment is furnished or not')\n",
+        "plt.xlabel('Furnished(1) or Not furnished(0)')\n",
+        "plt.ylabel('Mean rent')\n",
+        "mean_rooms= df.groupby(['furniture'])['rent amount (R$)'].mean().plot.bar()\n",
+        "\n",
+        "# print(len(mean_rooms))\n",
+        "# values = list(mean_rooms.values)\n",
+        "# names= list(mean_rooms.keys)\n",
+        "# print((values))\n",
+        "# names= list(mean_rooms.keys())\n",
+        "# x=[100*i for i in range(0,12)]\n",
+        "# x.append(1600)\n",
+        "# x.append(2000)\n",
+        "# x.append(12700)\n",
+        "# x.append(24600)\n",
+        "# x.append(46300)\n",
+        "# print(mean_rooms)\n",
+        "# print(x)\n",
+        "# print(len(values))\n",
+        "# plt.bar(x,values)\n",
+        "# print(type(mean_rooms))\n",
+        "# .plot(color = 'red',\n",
+        "        #  linestyle = 'solid', marker = 'o',legend=True)\n",
+        "# plt.hist(ar)\n",
+        "\n",
+        "df['area']= area_col\n",
+        "\n",
+        "\n",
+        "# plt.figure()\n",
+        "# plt.title('Variation of House association tax with number of rooms')\n",
+        "# mean_rooms= df.groupby(['rooms'])['hoa (R$)'].mean().plot(color = 'blue',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True) #doesn't reeally seem to have an association \n",
+        "# plt.figure()\n",
+        "# plt.title('Variation of property tax with number of rooms')\n",
+        "# mean_rooms= df.groupby(['rooms'])['property tax (R$)'].mean().plot(color = 'blue',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True) #doesn't reeally seem to have an association \n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "WK_u47GLP2pq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "669c5565-c01f-40b9-c63e-a11ec72b6887"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 792x1080 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1152x1080 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Rent clearly increases with increase in number of rooms and floors. Similary with area. Not really a clear correspondence with furnishing."
+      ],
+      "metadata": {
+        "id": "e_eqJSo-zsBZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#dependancies \n",
+        "#maybe try to find a dependency of room rent on number of rooms? \n",
+        "#can plot the mean of rent \n",
+        "plt.figure()\n",
+        "plt.title('Variation of House Association Tax with number of rooms')\n",
+        "mean_rooms= df.groupby(['rooms'])['hoa (R$)'].mean().plot.bar()\n",
+        "plt.figure(figsize=(11,15))\n",
+        "plt.title('Variation of House Association Tax with number of floors')\n",
+        "plt.xlim([0, 51])\n",
+        "# mean_rooms= df.groupby(['floor'])['rent amount (R$)'].mean().plot(color = 'red',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_rooms= df.groupby(['floor'])['rent amount (R$)'].mean().plot.bar()\n",
+        "\n",
+        "area_col_rounded= np.round(df['area'], -2)\n",
+        "area_col=df['area']\n",
+        "plt.figure()\n",
+        "df['area']= area_col_rounded\n",
+        "plt.title('Variation of House Association Tax with area')\n",
+        "plt.xlim([0, 1200])\n",
+        "# mean_rooms= df.groupby(['area'])['rent amount (R$)'].mean().plot(color = 'red',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_rooms= df.groupby(['area'])['hoa (R$)'].mean().plot.bar(color ='green',\n",
+        "        edgecolor ='yellow', label ='Area')\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.title('Variation of House Association Tax on whether apartment is furnished or not')\n",
+        "plt.xlabel('Furnished(1) or Not furnished(0)')\n",
+        "plt.ylabel('Mean rent')\n",
+        "mean_rooms= df.groupby(['furniture'])['hoa (R$)'].mean().plot.bar()\n",
+        "\n",
+        "df['area']= area_col\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "CApEEQq5b3sB",
+        "outputId": "b8aa1575-5979-41c6-c5f0-2694c5e8baf2"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 792x1080 with 1 Axes>"
+            ],
+            "image/png": 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ITQAAWghNAABaCE0AAFoITQAAWghNAABabFrvAQAA7K22bL1w2WWuOfOkPTCSjckeTQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFosG5pVdU5V3VhVH1pk3s9V1aiqB063q6peXlXbquqDVfXouWVPraqrpsupc9MfU1VXTOu8vKpqdz04AADWz0r2aL42yYkLJ1bV4UlOSPLJuclPSnLUdDk9yaumZQ9KckaSxyY5NskZVXXgtM6rkjxnbr073RcAAHufZUNzjPHuJDcvMuusJM9PMuamnZzk3DFzSZIDqupBSZ6Y5OIxxs1jjFuSXJzkxGneA8YYl4wxRpJzk5yytocEAMBGsKrPaFbVyUmuH2P8/YJZhya5du72ddO0nU2/bpHpS93v6VV1eVVdvn379tUMHQCAPWSXQ7Oq7pvk55P80u4fzs6NMc4eYxwzxjhm8+bNe/ruAQDYBavZo/nQJEcm+fuquibJYUneV1Vfm+T6JIfPLXvYNG1n0w9bZDoAAHu5XQ7NMcYVY4yvGWNsGWNsyexw96PHGJ9KckGSZ05nnx+X5LNjjBuSXJTkhKo6cDoJ6IQkF03zbquq46azzZ+Z5Pzd9NgAAFhHK/n1Rm9M8ndJHlFV11XVaTtZ/K1Jrk6yLcnvJnlukowxbk7ykiSXTZcXT9MyLfPqaZ1/TPLnq3soAABsJJuWW2CM8fRl5m+Zuz6SPG+J5c5Jcs4i0y9P8sjlxgEAwN7FXwYCAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKDFsqFZVedU1Y1V9aG5ab9eVR+tqg9W1R9X1QFz815YVduq6mNV9cS56SdO07ZV1da56UdW1aXT9DdX1T135wMEAGB9rGSP5muTnLhg2sVJHjnG+KYk/5DkhUlSVUcneVqSb5jW+e2q2qeq9knyyiRPSnJ0kqdPyybJryU5a4zxsCS3JDltTY8IAIANYdnQHGO8O8nNC6b9xRjj9unmJUkOm66fnORNY4x/HWN8PMm2JMdOl21jjKvHGF9K8qYkJ1dVJXl8krdM678uySlrfEwAAGwAu+Mzmj+c5M+n64cmuXZu3nXTtKWmH5zk1rlo3TEdAIC93JpCs6r+R5Lbk7xh9wxn2fs7vaour6rLt2/fvifuEgCAVVp1aFbVs5I8JckPjTHGNPn6JIfPLXbYNG2p6TclOaCqNi2YvqgxxtljjGPGGMds3rx5tUMHAGAPWFVoVtWJSZ6f5HvGGJ+fm3VBkqdV1b2q6sgkRyV5T5LLkhw1nWF+z8xOGLpgCtR3JnnqtP6pSc5f3UMBAGAjWcmvN3pjkr9L8oiquq6qTkvyiiT7Jbm4qj5QVb+TJGOMDyc5L8lHkrwtyfPGGF+ZPoP540kuSnJlkvOmZZPkBUl+tqq2ZfaZzdfs1kcIAMC62LTcAmOMpy8yeckYHGO8LMnLFpn+1iRvXWT61ZmdlQ4AwF2IvwwEAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQItlQ7OqzqmqG6vqQ3PTDqqqi6vqqunfA6fpVVUvr6ptVfXBqnr03DqnTstfVVWnzk1/TFVdMa3z8qqq3f0gAQDY81ayR/O1SU5cMG1rkrePMY5K8vbpdpI8KclR0+X0JK9KZmGa5Iwkj01ybJIzdsTptMxz5tZbeF8AAOyFlg3NMca7k9y8YPLJSV43XX9dklPmpp87Zi5JckBVPSjJE5NcPMa4eYxxS5KLk5w4zXvAGOOSMcZIcu7ctgAA2IttWuV6h4wxbpiufyrJIdP1Q5NcO7fcddO0nU2/bpHpi6qq0zPbU5ojjjhilUMHANg4tmy9cNllrjnzpD0wkt1vzScDTXsix24Yy0ru6+wxxjFjjGM2b968J+4SAIBVWm1ofno67J3p3xun6dcnOXxuucOmaTubftgi0wEA2MutNjQvSLLjzPFTk5w/N/2Z09nnxyX57HSI/aIkJ1TVgdNJQCckuWiad1tVHTedbf7MuW0BALAXW/YzmlX1xiTHJ3lgVV2X2dnjZyY5r6pOS/KJJD8wLf7WJE9Osi3J55M8O0nGGDdX1UuSXDYt9+Ixxo4TjJ6b2Znt90ny59MFAIC93LKhOcZ4+hKznrDIsiPJ85bYzjlJzllk+uVJHrncOAAA2Lv4y0AAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC02LTeAwAA2BVbtl640/nXnHnSHhoJy7FHEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBab1nsAAMDdw5atFy67zDVnnrQHRsKeYo8mAAAthCYAAC2EJgAALYQmAAAthCYAAC2EJgAALYQmAAAthCYAAC2EJgAALYQmAAAthCYAAC2EJgAALYQmAAAthCYAAC2EJgAALYQmAAAthCYAAC2EJgAALYQmAAAthCYAAC3WFJpV9TNV9eGq+lBVvbGq7l1VR1bVpVW1rareXFX3nJa913R72zR/y9x2XjhN/1hVPXFtDwkAgI1g1aFZVYcm+ckkx4wxHplknyRPS/JrSc4aYzwsyS1JTptWOS3JLdP0s6blUlVHT+t9Q5ITk/x2Ve2z2nEBALAxrPXQ+aYk96mqTUnum+SGJI9P8pZp/uuSnDJdP3m6nWn+E6qqpulvGmP86xjj40m2JTl2jeMCAGCdrTo0xxjXJ/lfST6ZWWB+Nsl7k9w6xrh9Wuy6JIdO1w9Ncu207u3T8gfPT19kHQAA9lJrOXR+YGZ7I49M8nVJ7pfZoe82VXV6VV1eVZdv3769864AAFijtRw6/+4kHx9jbB9jfDnJHyV5XJIDpkPpSXJYkuun69cnOTxJpvn7J7lpfvoi69zBGOPsMcYxY4xjNm/evIahAwDQbS2h+ckkx1XVfafPWj4hyUeSvDPJU6dlTk1y/nT9gul2pvnvGGOMafrTprPSj0xyVJL3rGFcAABsAJuWX2RxY4xLq+otSd6X5PYk709ydpILk7ypql46TXvNtMprkry+qrYluTmzM80zxvhwVZ2XWaTenuR5Y4yvrHZcAABsDKsOzSQZY5yR5IwFk6/OImeNjzG+mOT7l9jOy5K8bC1jAQBgY/GXgQAAaCE0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaLGmP0EJANw9bNl64U7nX3PmSXtoJOxN7NEEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKDFpvUeAAB3b1u2XrjsMtecedIeGAmwu9mjCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQItN6z0AAPZeW7ZeuOwy15x50h4YCbAR2aMJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAizWFZlUdUFVvqaqPVtWVVfVtVXVQVV1cVVdN/x44LVtV9fKq2lZVH6yqR89t59Rp+auq6tS1PigAANbfWvdo/maSt40xvj7JNye5MsnWJG8fYxyV5O3T7SR5UpKjpsvpSV6VJFV1UJIzkjw2ybFJztgRpwAA7L1W/bfOq2r/JP8xybOSZIzxpSRfqqqTkxw/Lfa6JO9K8oIkJyc5d4wxklwy7Q190LTsxWOMm6ftXpzkxCRvXO3YALj78XfXYeNZyx7NI5NsT/J7VfX+qnp1Vd0vySFjjBumZT6V5JDp+qFJrp1b/7pp2lLT76SqTq+qy6vq8u3bt69h6AAAdFtLaG5K8ugkrxpjfEuSf8m/HyZPkkx7L8ca7uMOxhhnjzGOGWMcs3nz5t21WQAAGqwlNK9Lct0Y49Lp9lsyC89PT4fEM/174zT/+iSHz61/2DRtqekAAOzFVh2aY4xPJXMnToQAABiHSURBVLm2qh4xTXpCko8kuSDJjjPHT01y/nT9giTPnM4+Py7JZ6dD7BclOaGqDpxOAjphmgYAwF5s1ScDTX4iyRuq6p5Jrk7y7Mzi9byqOi3JJ5L8wLTsW5M8Ocm2JJ+fls0Y4+aqekmSy6blXrzjxCAAAPZeawrNMcYHkhyzyKwnLLLsSPK8JbZzTpJz1jIWAAA2Fn8ZCACAFms9dA4AsNfxe1f3DHs0AQBoITQBAGghNAEAaCE0AQBo4WQggD3EyQfA3Y09mgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALTYtN4DAGDP27L1wmWXuebMk/bASIC7Mns0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaLFpvQcAwK7ZsvXCZZe55syT9sBI7no8t7B72aMJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAi03rPQCATlu2XrjsMtecedIeGAnA3c9dJjR9MwEA2FgcOgcAoIXQBACghdAEAKCF0AQAoIXQBACghdAEAKCF0AQAoMVd5vdoAsBG4Xc7w4w9mgAAtBCaAAC0EJoAALQQmgAAtHAyEMAKLHdyhxM7AO7MHk0AAFoITQAAWjh0DmxYDlcD7N3s0QQAoIU9msBu56+iAJDYowkAQBOhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALv94IuAO/JB2A3cUeTQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABaCE0AAFoITQAAWghNAABabFrvAcDebMvWC5dd5pozT9oDIwGAjcceTQAAWtijCXcR9q4CsNGseY9mVe1TVe+vqj+bbh9ZVZdW1baqenNV3XOafq/p9rZp/pa5bbxwmv6xqnriWscEAMD62x2Hzn8qyZVzt38tyVljjIcluSXJadP005LcMk0/a1ouVXV0kqcl+YYkJyb57araZzeMCwCAdbSm0Kyqw5KclOTV0+1K8vgkb5kWeV2SU6brJ0+3M81/wrT8yUneNMb41zHGx5NsS3LsWsYFAMD6W+tnNH8jyfOT7DfdPjjJrWOM26fb1yU5dLp+aJJrk2SMcXtVfXZa/tAkl8xtc36dO6iq05OcniRHHHHEGocOG4PPVgKd/B/Delr1Hs2qekqSG8cY792N49mpMcbZY4xjxhjHbN68eU/dLQAAq7CWPZqPS/I9VfXkJPdO8oAkv5nkgKraNO3VPCzJ9dPy1yc5PMl1VbUpyf5JbpqbvsP8OtDCT/gA0G/VezTHGC8cYxw2xtiS2ck87xhj/FCSdyZ56rTYqUnOn65fMN3ONP8dY4wxTX/adFb6kUmOSvKe1Y4LAICNoeP3aL4gyZuq6qVJ3p/kNdP01yR5fVVtS3JzZnGaMcaHq+q8JB9JcnuS540xvtIwLgAA9qDdEppjjHcledd0/eosctb4GOOLSb5/ifVfluRlu2MsAABsDP4yEABsUMt9ntxnydno/K1zAABaCE0AAFoITQAAWghNAABaOBmIvY5ftg4Aewd7NAEAaCE0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaCE0AQBoITQBAGghNAEAaOFPULIi/uwjALCr7NEEAKCFPZrsMfaKAsDdiz2aAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALQQmgAAtBCaAAC0EJoAALTYtN4DoNeWrRcuu8w1Z560B0YCANzd2KMJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAC6EJAEALoQkAQAuhCQBAi03rPQCWtmXrhTudf82ZJ+2hkQAA7Dp7NAEAaGGP5gL2IgIA7B72aAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0MLv0Wyw3O/iTPw+TgDgrs8eTQAAWghNAABaCE0AAFr4jCYAAEl2/3km9mgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQQmgCANBCaAIA0EJoAgDQYtWhWVWHV9U7q+ojVfXhqvqpafpBVXVxVV01/XvgNL2q6uVVta2qPlhVj57b1qnT8ldV1alrf1gAAKy3tezRvD3Jz40xjk5yXJLnVdXRSbYmefsY46gkb59uJ8mTkhw1XU5P8qpkFqZJzkjy2CTHJjljR5wCALD3WnVojjFuGGO8b7r+uSRXJjk0yclJXjct9rokp0zXT05y7pi5JMkBVfWgJE9McvEY4+Yxxi1JLk5y4mrHBQDAxrBbPqNZVVuSfEuSS5McMsa4YZr1qSSHTNcPTXLt3GrXTdOWmr7Y/ZxeVZdX1eXbt2/fHUMHAKDJmkOzqu6f5A+T/PQY47b5eWOMkWSs9T7mtnf2GOOYMcYxmzdv3l2bBQCgwZpCs6r2zSwy3zDG+KNp8qenQ+KZ/r1xmn59ksPnVj9smrbUdAAA9mJrOeu8krwmyZVjjP8zN+uCJDvOHD81yflz0585nX1+XJLPTofYL0pyQlUdOJ0EdMI0DQCAvdimNaz7uCTPSHJFVX1gmvbzSc5Mcl5VnZbkE0l+YJr31iRPTrItyeeTPDtJxhg3V9VLklw2LffiMcbNaxgXAAAbwKpDc4zx10lqidlPWGT5keR5S2zrnCTnrHYsAABsPGvZowkAwAaxZeuFyy5zzZkn7YGR/Dt/ghIAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBZCEwCAFkITAIAWQhMAgBYbJjSr6sSq+lhVbauqres9HgAA1mZDhGZV7ZPklUmelOToJE+vqqPXd1QAAKzFhgjNJMcm2TbGuHqM8aUkb0py8jqPCQCANagxxnqPIVX11CQnjjF+ZLr9jCSPHWP8+ILlTk9y+nTzEUk+tpPNPjDJZ3bD8DbSdozFWIxl42zHWIzFWDbOdoxl/cfy4DHG5oUTN+2GO99jxhhnJzl7JctW1eVjjGPWep8baTvGYizGsnG2YyzGYiwbZzvGsnHHslEOnV+f5PC524dN0wAA2EttlNC8LMlRVXVkVd0zydOSXLDOYwIAYA02xKHzMcbtVfXjSS5Ksk+Sc8YYH17jZld0iH0v246x9G1jd23HWPq2sdG2Yyx929hd2zGWvm1stO0YS9821rSdDXEyEAAAdz0b5dA5AAB3MUITAIAWQhMAgBYb4mSg3aGqvj6zvyZ06DTp+iQXjDGuXKexHJrk0jHGP89NP3GM8bZd2M6xScYY47LpT3KemOSjY4y3rmFs544xnrna9adtfEdmf83pQ2OMv1jhOo9NcuUY47aquk+SrUkeneQjSX5ljPHZFW7nJ5P88Rjj2tWNPpn7zQb/NMb4y6r6wSTfnuTKJGePMb68C9t6SJLvzezXc30lyT8k+YMxxm2rHR9w11NVXzPGuHG9x5EkVXXwGOOm9R4Hdw93iT2aVfWCzP5sZSV5z3SpJG+sqq276T6evcLlfjLJ+Ul+IsmHqmr+T2n+yi7c3xlJXp7kVVX1q0lekeR+SbZW1f9Y4TYuWHD50yTfu+P2LozlPXPXnzONZb8kZ+zC83tOks9P138zyf5Jfm2a9nsrHUuSlyS5tKr+v6p6blXd6a8QrMDvJTkpyU9V1euTfH+SS5N8a5JXr3Qj02v9O0nuPa17r8yC85KqOn4V42IZVfU16z2GHarq4PUew3qrqv2r6syq+mhV3VxVN1XVldO0A3bD9v98F5Z9QFX9alW9fvrhcX7eb+/Cdr62ql5VVa+sqoOr6kVVdUVVnVdVD1rhNg5acDk4yXuq6sCqOmgXxnLi3PX9q+o1VfXBqvqDqjpkhds4s6oeOF0/pqquzuz/0E9U1XftwljeV1W/UFUPXek6i2zjmKp6Z1X9flUdXlUXV9Vnq+qyqvqWXdjO/avqxVX14Wn97VV1SVU9axe2sU9V/WhVvaSqHrdg3i/swna+ae76vtNzdEFV/UpV3XeF2/jxudfoYVX17qq6taourapvXOlYlrmPFX8t7WQbV6xqxTHGXn/JbC/SvotMv2eSq3bTfXxyhctdkeT+0/UtSS5P8lPT7ffvwv1dkdmverpvktuSPGCafp8kH1zhNt6X5PeTHJ/ku6Z/b5iuf9cujOX9c9cvS7J5un6/JFescBtXzo9rwbwP7MpYMvsB6YQkr0myPcnbkpyaZL8VbuOD07+bknw6yT7T7Vrpczv/Gk3X75vkXdP1I3bxtd4/yZlJPprk5iQ3ZbZ39cwk/397Zx9rR1HG4ect1zQ2DeXSmrakUkSpRZFWqeXDGlFQi5paAyR+/NMmisSk1UKwxEqqBIIhWqIiEIG0UC3WUgOJUkFbBAXaoqbtBS4QoC2gUGtA5aPBfox/vHPbZTnn3pmze+49F39PMjlzZs/8zrs7szPvzs7OHlFT/V2X+LvDgSuAlcAXS9uuyfi/CcC1wE+AscB34vH6JTAxUePIUhgL7AC6gSMzbJldOtY3AtuAVcD4RI3vAeNifAbwFPAEsDPzXPor8G3gnRXKcgZwdzy33w78Dvh3PDffn6EzGrgUeDjm3w1sBOZlaNwJLAYmlMp+MXBXosYHmoSTgOcybFkby2kuvg7zWmBk33HP0PktPlBwcawni+NxXgDcnqhxANheCnvj51M59aUQvwG4DJgMLAJuS9ToKcTvBj4Y41OAP2fYsh34PvA0PpizCDgqs+5uBs4CvgA8A5wT088AHsjQuR2Yh7/c5QLgEuA44Cb8DlmKxg2xDfgG8BdgWaPjnllGPwBW4H3sVcDNiRoPF+K/AT4X46cD92XYUvlcwu/QNQpnA7tzyvugZiuZOi3gHfTkBumTgccydLY1CT3Aa7kVJn4fHRuuZWQ6VI3i8XuSDu6QLcI7oukxLbmRK+hsxTv3seWGqWxbPxprgPkxvhyYEeNTgAczbCk7qW8B5gC3pJ4EwEP4RUg38BLRacFHJnszbOnhUEfWXTw2+LSCVJ3KnXXMU0cjo866SVkX4uqsD2k0bV/721b63X5gQzyu5bAnY3+2lL4vAe7D262cultse5/u7z/60bgwngPvK5Z9Tjk3qLvl/Uu1pRfoivGNpW1JAwUNbPkwcA3wfCyn82o4tjkX51tL3x+MnyPw6WUpGtsK8S58nchf4Xemcmwp7tMW4qAXGQMXxXOFUn+YqhF/W/lcwtvYFXg/XQ4v5dbhEN48juZsfHRhXawsP40n+RMURjMSdHYB0/FOqBiOwefzpWhsIDp1hbQu4GZgf4Ytm4BRMT6ikD4mp9GMeSbhjt7V5ZM7Mf8OfARne/ycGNNHZzR2Y2LlfTLu296odQ8wLcOWpg1A3/FK0FgU/3snsBBYD1yPO45LM2z5Ou5EXY9f7PQ50m8D7s3QqdxZx9/W0cios26soc66scZdwDcpjAwD4/ELi98najwEHNdk2zMZ+9NLoa2MafPwEdudrRwX4LIKZd3X7i7Dpxq1cpH/LH4RcGFss6ywLdWJWRDL6WP4nYUf4iNu3wVWtlJ3C2mH4f3v8kSNB/C7Uefi7e/cmP4R8i7Y7gdmxfgc4M7CttQLnDfUcWBpbO+S74TGcukb8estbduaqHE53j8eC3wLH2WdDMwHfp1hS+VzCR/dPaGKxhvytZKpE0NsHE+JhX12jB+WqXFjX+VtsG1VosYkCiNTpW0fyrBlZJP0cRQ63sz9+zSJIxWJeqOAd2TmORyYho+yJd22LOWfUpPtRxFHkoAjgHOAmS3ovDfmnVrBlsqddcxTRyOjzrqxhjrrxhrd+FzrR4EX8akfvTEtaXpDPH/e3WTb3Iz9uRI4s0H6bPIch0uJ059K6e8Cbm2h/s3BpyQ830LepaXQN21pAom3ZePvTwdW41OPeoA7gPNoMOWsH41f5NrfQGMafgdnHTA1nkf/iu3LaZk6m2Od+1Nf/cEv8hcmavyMBgNRwJeBvRm2LC+F8YUyWp+hMx8fhPknfqftEfy5jjEZGpXPJfwC+Ogm22a0VO5VK46CgkK1UOqsXyh11t0ZOnU0Muqsm+s066y7MjQ6qbM+sdRZT4npyZ11/P1U4MxyeTfqxAfQOKOKxgA6Z9Wk09I+4XPrT6h5nzrp+ObYcnxNthxfQ72byaFpMO/BL0g/lWNHzHtyHTolzeQL2H40ZkVbPlFVq5IdQ/nnCgoK/Qfi7fhO0BlqW0qdtY5LB9iCTz15DLgNn2Lz2cK2pKkWdWjE3y7oFJ1O2qcOtOXRGmyprINfdG7EH9i9Ap92dAlwL7Akw5ayzvpcHXw+fDm83BfPsGVzIf4VfM7oUnw6wMWJGl3AV/FpS33PqawDzidjBPx1mq1kUlBQGJxAC3Nq26UjW2RLg99VXmWjDo1O05Etw8aWSiu71KVDZ60Qcwu+Ysgp+NSlSTF+LbA61ZZieNMs2C7EcMXMtjXbhM/VHDQd2SJbMnVGhPhSihDCjrh+7K1mNjnqDJZGp+nIls63ZV8IYT/wqpk9GeJLNkIIe8zsQIYtdejMwB8uXQJcFELYYmZ7Qgj3ZNgBMMLMuvFnViyEsDva8oqZ7UvUOCmEMKWU9iy+PvTjmfYAb6I3AwkxjBkPfBKfK1fE8Ac2BlNHtsiWHJ1dZjY9hLAFIITwspl9Bn9BQ+pC03VodJqObOl8W/5rZqNCCK/iD6cCvjA+vrRaKpV1QggHgKvMbE383EVr/tkY/KlxA4KZTQwhPGdmo0l3wF8ws3OBtdEuzGwE/vBhua1Io5VhUAUFhfoCNax2UJeObJEtmbZUXmWjDo1O05Etw8KWWlZ2qUunlHfIVojBpyKsBv6Bvwzn8RhfnapRDhaFhRBCCCHE/zlmdjIQ8HWvpwKnAo+EEO5oSU+OphBCCCGEMLOl+JvHuvC3Cs4E/gB8HF9r9/JsTTmaQgghhBDCzHrwNySOxN9aNimE8B8zeyuwKYRwYq7miJptFEIIIYQQw5N9IYT9wR9uet1T9OQ9JHUQOZpCCCGEEALiU/QxXuVp/IPo1rkQQgghhMDMRoYQXmuQPg6YGELoydaUoymEEEIIIdqBbp0LIYQQQoi2IEdTCCGEEEK0BTmaQgjRBsxsoZn1mtnfzOzqobZHCCGGAjmaQgjRHr6GL3K8pA4xM2vl3cdCCDGkyNEUQoiaMbPrgGOBdUB3If0YM9tgZtvMbL2ZHT1A+gozu87MNgFXDsW+CCFEFeRoCiFEzYQQzgf+DnwUeLGw6cfATfHtGj8HfjRAOsAk4LQQwgVtN1wIIWpGjqYQQgwepwKrYnwlMGuAdIA1IYT9g2OeEELUixxNIYTobF4ZagOEEKJV5GgKIcTgcT/w+Rj/EvDHAdKFEGJYo6cYhRBi8FgALDezi4DdwPwB0oUQYlijV1AKIYQQQoi2oFvnQgghhBCiLcjRFEIIIYQQbUGOphBCCCGEaAtyNIUQQgghRFuQoymEEEIIIdqCHE0hhBBCCNEW5GgKIYQQQoi2IEdTCCGEEEK0hf8Bwahmp5psz18AAAAASUVORK5CYII=\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "House association tax seems to increase with number of floors(maybe more likely to have a stronger association with an increase in number of floors). No clear dependence on other factors. "
+      ],
+      "metadata": {
+        "id": "Ewo9MzNhz-3s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#dependancies \n",
+        "#maybe try to find a dependency of room rent on number of rooms? \n",
+        "#can plot the mean of rent \n",
+        "plt.figure()\n",
+        "plt.title('Variation of Property Tax with number of rooms')\n",
+        "mean_rooms= df.groupby(['rooms'])['property tax (R$)'].mean().plot.bar()\n",
+        "plt.figure(figsize=(11,15))\n",
+        "plt.title('Variation of Property Tax with number of floors')\n",
+        "plt.xlim([0, 51])\n",
+        "# mean_rooms= df.groupby(['floor'])['rent amount (R$)'].mean().plot(color = 'red',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_rooms= df.groupby(['floor'])['property tax (R$)'].mean().plot.bar()\n",
+        "\n",
+        "area_col_rounded= np.round(df['area'], -2)\n",
+        "area_col=df['area']\n",
+        "df['area']= area_col_rounded\n",
+        "plt.figure(figsize=(16,15))\n",
+        "plt.title('Variation of Property Tax with area')\n",
+        "plt.xlim([0, 1200])\n",
+        "# mean_rooms= df.groupby(['area'])['rent amount (R$)'].mean().plot(color = 'red',\n",
+        "#          linestyle = 'solid', marker = 'o',legend=True)\n",
+        "mean_rooms= df.groupby(['area'])['property tax (R$)'].mean().plot.bar(color ='green',\n",
+        "        edgecolor ='yellow', label ='Area')\n",
+        "\n",
+        "plt.figure()\n",
+        "plt.title('Variation of Property Tax on whether apartment is furnished or not')\n",
+        "plt.xlabel('Furnished(1) or Not furnished(0)')\n",
+        "plt.ylabel('Mean rent')\n",
+        "mean_rooms= df.groupby(['furniture'])['property tax (R$)'].mean().plot.bar()\n",
+        "\n",
+        "df['area']= area_col\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "TBD3b1FHherQ",
+        "outputId": "0f15c337-5c8a-4127-f9bb-d21d229b633f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 792x1080 with 1 Axes>"
+            ],
+            "image/png": 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krx5j3CrJq+fbSXKvJLeah9OTPDmZAmiSM5LcKckdk5yxL4QCAHBoWzdQjjHekOTyFZPvk+TMefzMJKcsTH/2mLwlyRFVdVyS/5TkVWOMy8cYn07yqlw5pAIAcAja7DmUx44xPjaPfzzJsfP48UkuXljuknnaWtOvpKpOr6pzq+rcvXv3brI8AAC2S/uinDHGSDK2oJZ97T1ljHHSGOOkY445ZquaBQDgKrLZQHnZfCg7899PzNMvTXLiwnInzNPWmg4AwCFus4HyrCT7rtQ+NclLF6Y/YL7a+85JPjMfGn9Fkh+vqiPni3F+fJ4GAMAhbsd6C1TV85PcLcmNq+qSTFdr707ywqo6LclFSX5mXvxlSX4iyYVJvpDkQUkyxri8qv4gydvm5R49xlh5oQ8AAIegdQPlGOP+a8y6+yrLjiQPXqOdZyR5xoaqAwDgoOeXcgAAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGjZcaAL4Opn566z111mz+6Tt6ESAGA76KEEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKClFSir6req6j1V9e6qen5VXbeqblFV51TVhVX1gqr61nnZ68y3L5zn79yKOwAAwIG16UBZVccneWiSk8YY35Pk2kl+LsnjkjxhjHHLJJ9Octq8ymlJPj1Pf8K8HAAAh7juIe8dSa5XVTuSXD/Jx5L8WJIXzfPPTHLKPH6f+Xbm+XevqmpuHwCAA2zTgXKMcWmSP07ykUxB8jNJ3p7kijHGV+bFLkly/Dx+fJKL53W/Mi9/9Ga3DwDAwWHHZlesqiMz9TreIskVSf4myT27BVXV6UlOT5Kb3exm3eYAgEPQzl1nr7vMnt0nb0MlLKNzyPseST48xtg7xvhykr9NcpckR8yHwJPkhCSXzuOXJjkxSeb5hyf51MpGxxhPGWOcNMY46ZhjjmmUBwDAdugEyo8kuXNVXX8+F/LuSd6b5LVJ7jsvc2qSl87jZ823M89/zRhjNLYPAMBBoHMO5TmZLq75lyTnz209Jckjk/x2VV2Y6RzJp8+rPD3J0fP0306yq1E3AAAHiU2fQ5kkY4wzkpyxYvKHktxxlWW/lOR+ne0BAHDwaQVKAFiWiyzg6stPLwIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQIlACANAiUAIA0CJQAgDQsuNAFwAAG7Fz19n7nb9n98nbVAmwjx5KAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWnYc6AIAuOrs3HX2usvs2X3yNlQCXJ3poQQAoEWgBACgRaAEAKDFOZQAW8x5i8A1jR5KAABa9FACsF96XIH16KEEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKClFSir6oiqelFVva+qLqiqH6qqo6rqVVX1gfnvkfOyVVV/VlUXVtV5VXWHrbkLAAAcSN0eyicm+ccxxm2SfH+SC5LsSvLqMcatkrx6vp0k90pyq3k4PcmTm9sGAOAgsOlAWVWHJ/k/kjw9ScYY/zHGuCLJfZKcOS92ZpJT5vH7JHn2mLwlyRFVddymKwcA4KDQ6aG8RZK9SZ5ZVe+oqqdV1Q2SHDvG+Ni8zMeTHDuPH5/k4oX1L5mnfZOqOr2qzq2qc/fu3dsoDwCA7dAJlDuS3CHJk8cYt0/yb/nG4e0kyRhjJBkbaXSM8ZQxxkljjJOOOeaYRnkAAGyHTqC8JMklY4xz5tsvyhQwL9t3KHv++4l5/qVJTlxY/4R5GgAAh7BNB8oxxseTXFxV3zlPunuS9yY5K8mp87RTk7x0Hj8ryQPmq73vnOQzC4fGAQA4RO1orv+QJH9VVd+a5ENJHpQppL6wqk5LclGSn5mXfVmSn0hyYZIvzMsCAHCIawXKMcY7k5y0yqy7r7LsSPLgzvYAADj4+KUcAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABadhzoAgBgu+3cdfa6y+zZffI2VAJXD3ooAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaNlxoAsAgEPVzl1n73f+nt0nb1MlcGDpoQQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoMX3UAJXC+t9H2DiOwEBrip6KAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaPE9lAAz32UJsDkCJVwDCU4AbCWHvAEAaBEoAQBoESgBAGgRKAEAaHFRDrBp613c48IegGsGPZQAALQIlAAAtAiUAAC0CJQAALQcchfluAgAAODgoocSAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAICWHQe6AGBjdu46e7/z9+w+eZsqAYCJHkoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABa2oGyqq5dVe+oqn+Yb9+iqs6pqgur6gVV9a3z9OvMty+c5+/sbhsAgANvK3ooH5bkgoXbj0vyhDHGLZN8Oslp8/TTknx6nv6EeTkAAA5xrUBZVSckOTnJ0+bbleTHkrxoXuTMJKfM4/eZb2eef/d5eQAADmHdHso/TfKIJF+bbx+d5Ioxxlfm25ckOX4ePz7JxUkyz//MvDwAAIewTQfKqrp3kk+MMd6+hfWkqk6vqnOr6ty9e/duZdMAAFwFOj2Ud0nyU1W1J8lfZzrU/cQkR1TVjnmZE5JcOo9fmuTEJJnnH57kUysbHWM8ZYxx0hjjpGOOOaZRHgAA22HTgXKM8TtjjBPGGDuT/FyS14wxfiHJa5Pcd17s1CQvncfPmm9nnv+aMcbY7PYBADg4XBXfQ/nIJL9dVRdmOkfy6fP0pyc5ep7+20l2XQXbBgBgm+1Yf5H1jTFel+R18/iHktxxlWW+lOR+W7E9AAAOHlsSKIH17dx19rrL7Nl98jZUAgBby08vAgDQoocS4CCkRxs4lOihBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBlx4EuAK5KO3edve4ye3afvA2VAMDVlx5KAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWgRKAABaBEoAAFoESgAAWvxSDixhvV/c8Ws7AFyT6aEEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBlx4EuALhm27nr7HWX2bP75G2oBIDN0kMJAECLQAkAQItACQBAi3MoOSg5rw4ADh16KAEAaBEoAQBoccgbAEjidCM2Tw8lAAAtAiUAAC0CJQAALQIlAAAtAiUAAC0CJQAALQIlAAAtAiUAAC0CJQAALQIlAAAtAiUAAC1+yxsArgbW+x1uv8HNVUkPJQAALQIlAAAtAiUAAC0CJQAALS7K4evWO6E7cVI3AHBleigBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGjZcaALYGvs3HX2usvs2X3yNlQCAFzT6KEEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKDFTy8CAFdbfpp4e+ihBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBlx4Eu4EDYuevsdZfZs/vkbagEAODQp4cSAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAlmvk1wYdTHyFEQBwqNNDCQBAi0AJAECLQAkAQItACQBAy6YDZVWdWFWvrar3VtV7quph8/SjqupVVfWB+e+R8/Sqqj+rqgur6ryqusNW3QkAAA6cTg/lV5I8fIxx2yR3TvLgqrptkl1JXj3GuFWSV8+3k+ReSW41D6cneXJj2wAAHCQ2/bVBY4yPJfnYPP65qrogyfFJ7pPkbvNiZyZ5XZJHztOfPcYYSd5SVUdU1XFzO4ckX/kDALBF51BW1c4kt09yTpJjF0Lix5McO48fn+TihdUumaetbOv0qjq3qs7du3fvVpQHAMBVqEuioaIAABGFSURBVB0oq+qwJC9O8ptjjM8uzpt7I8dG2htjPGWMcdIY46RjjjmmWx4AAFexVqCsqm/JFCb/aozxt/Pky6rquHn+cUk+MU+/NMmJC6ufME8DAOAQ1rnKu5I8PckFY4zHL8w6K8mp8/ipSV66MP0B89Xed07ymUP5/EkAACad3/K+S5JfSnJ+Vb1znva7SXYneWFVnZbkoiQ/M897WZKfSHJhki8keVBj2wAAHCQ6V3m/MUmtMfvuqyw/kjx4s9sDAODg5JdyAABoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBoESgBAGgRKAEAaBEoAQBo2XGgCwAAYHvt3HX2usvs2X3y0u3poQQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgRaAEAKBFoAQAoEWgBACgZceBLgAArsl27jp73WX27D55GyqBzdNDCQBAi0AJAECLQAkAQItACQBAi0AJAECLQAkAQItACQBAi0AJAECLLzYHADhEHKxfhK+HEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAFoESAIAWgRIAgBaBEgCAlm0PlFV1z6r616q6sKp2bff2AQDYWtsaKKvq2kn+PMm9ktw2yf2r6rbbWQMAAFtru3so75jkwjHGh8YY/5Hkr5PcZ5trAABgC9UYY/s2VnXfJPccY/zyfPuXktxpjPEbC8ucnuT0+eZ3JvnXdZq9cZJPbkF5W9GOWtRyqNayVe2oRS1qOXjaUYtatrqNm48xjlltxo7mxrfcGOMpSZ6y7PJVde4Y46TudreiHbWo5VCtZavaUYta1HLwtKMWtWxnG9t9yPvSJCcu3D5hngYAwCFquwPl25LcqqpuUVXfmuTnkpy1zTUAALCFtvWQ9xjjK1X1G0lekeTaSZ4xxnhPs9mlD49vQztquera2Kp21HLVtqOWq66NrWpHLVddGwdbO2q56trYqnauNrVs60U5AABc/filHAAAWgRKAABaBEoAAFoOuu+hXE9V3SbTr+scP0+6NMlZY4wLDlAtxyc5Z4zx+YXp9xxj/OOSbdwxyRhjvG3+Gcp7JnnfGONlzdqePcZ4QLONH8n060bvHmO8csl17pTkgjHGZ6vqekl2JblDkvcmecwY4zNLtvPQJH83xrh4c9UnC98k8NExxj9V1c8n+eEkFyR5yhjjyxto69uT/JdMX3v11STvT/K8McZnN1sfcPVTVd82xvjEga4jSarq6DHGpw50HVwzHFI9lFX1yEw/11hJ3joPleT5VbVri7bxoCWXe2iSlyZ5SJJ3V9XiT0g+Zsk2zkjyZ0meXFWPTfKkJDdIsquqfm8DNZ+1Yvj7JP9l3+0NtPPWhfFfmeu5YZIzNrB/n5HkC/P4E5McnuRx87RnLltLkj9Ick5V/X9V9etVteo386/jmUlOTvKwqnpOkvslOSfJDyZ52rKNzI/1Xya57rzudTIFy7dU1d02URfrqKpvO9A17FNVRx/oGg60qjq8qnZX1fuq6vKq+lRVXTBPO2IL2n/5Bpa9UVU9tqqeM/+TuDjvL5Zs4yZV9eSq+vOqOrqqHlVV51fVC6vquA3UctSK4egkb62qI6vqqCXbuOfC+OFV9fSqOq+qnldVx26glt1VdeN5/KSq+lCm99CLququS7bxL1X1+1X1Hctud412Tqqq11bVc6vqxKp6VVV9pqreVlW3X7KNw6rq0VX1nnndvVX1lqp64AZruXZV/WpV/UFV3WXFvN9fso3vWxj/lnkfnVVVj6mq62+glt9YeIxuWVVvqKorquqcqvreZdtZZxtLv5b208b5m1pxjHHIDJl6hb5llenfmuQDW7SNjyy53PlJDpvHdyY5N8nD5tvv2EAb105y/SSfTXKjefr1kpy3gZr/Jclzk9wtyV3nvx+bx++6gXbesTD+tiTHzOM3SHL+km1csFjXinnv3Egtmf7h+fEkT0+yN8k/Jjk1yQ2XbOO8+e+OJJclufZ8uza4f89fWPf6SV43j99s2cd6Xv7wJLuTvC/J5Uk+lam3dHeSI7bgufvyDSx7oySPTfKcJD+/Yt5fLNnGTZI8OcmfJzk6yaPmffXCJMdtoJajVgxHJ9mT5MgkRy3Zxj1X7OenJzkvyfOSHLuBWnYnufE8flKSDyW5MMlFy76W5tfj7yf5jubjeVKS186v7ROTvCrJZ+bX5u2XbOOwJI9O8p553b1J3pLkgRus5RVJHpnkJise/0cmeeWSbdxhjeEHknxsA7W8eH6cTsn0PcYvTnKdfft+yTb+MVNnwK75efLIeR8/JMlLN1DL15J8eMXw5fnvh5Z9viyMPy3JHya5eZLfSvKSDdRy/sL4a5P84Dx+6yTnLtnGh5P8cZKPZOqw+a0kN93Ec/etSe6V5P5JLk5y33n63ZP885JtvDTJAzP9AMpvJ/kfSW6V5MxMR7uWreVp8/vAbyZ5e5LHr7bvN/AY/UmSZ2X6fH1CkmdvoJb3LIyfneQ/z+N3S/KmDbTTfi1lOuK22vDTSfZu9DEfYxxygfJ9mX5HcuX0myf51w20c94aw/lJ/n2jT4z59mHzm9Tjs2RwyjcHuHesmLeR8HWt+YX/qiS3m6ct9Wa2op13ZfoQP3rlG9DK+vbTxt8kedA8/swkJ83jt07ytg3UsjKMfkuSn0ry/GWf7EnenemfjSOTfC5zOMnU03jBBmo5P9/4wDpycd9kOh1g2XZ8KK/ejg/l1ds5mD6U13x/3d+8Fct9Nclr5v26cvjiBmp554rbv5fkTZnet5Z97i6+935kf+2v087D59fB9y4+/ht8nP9lrW1vsJYLkuyYx9+yYt6yHQKLtfxokr9I8vH5MTp9A7Xsb/8u+1nyrhW33zb/vVam08KWreW8hfEdmb5r8W8zHWlatpbF+/POzB1b2XjnxL8ujL9trTqXaKf9Wsr0HvusTJ/TK4fPbeQ5/PU2N7PSgRoynV94YZKXz0+Kp8wv5guz0EOxRDuXJbldpg+cxWFnpvPtlmnjNZnD24on67OTfHXJNs5Jcv15/FoL0w9f9o1xRXsnZAp0T1r5Il5y/T2ZemQ+PP89bp5+2LJvbHPtz0rywfn+fXlu6/VJvn8Dtaz5Qt+3z5Zo47fmbV+U5KFJXp3kqZkC4hkbqOVhmQLTUzP9U7MvMB+T5A0baMeH8urt+FDe+P7d7g/lVyZ5RBZ6e5Mcm+mfiH9aso13J7nVGvMu3uBjdK0V0x6YqRf2oo3ulyR/uJnHeWH5fe+7j890itCG/plPckmmsP/w+f2qFuZtJGQ8ZH6cfizT0YInZupF+19JnrPR5+7CtGtn+ux95gZq+edMR5ful+n995R5+l2z/D9mb07yI/P4TyV5xcK8jXQgXel5nuSM+f1uqSOb8+Oyr/fughXz3rWBWv4o0+fjtyf53Uy9pjdP8qAk/7CBdtqvpUy9td/TaeNK621mpQM5zG+Ed54f2J+ex6+9wTaevu+Jusq85y3ZxglZ6GlaMe8uS7ZxnTWm3zgLH66b2EcnZwO9D0u0d/0kt9jgOjdK8v2Zes6WPuS4sP6tt6j2m2buHUpyRJL7JrnjJtr57nnd2zRq8aG8dls+lK+8zsH0oXxkpnOh35fk05lO2bhgnrbsaQn3TfKda8w7ZQO1/N9J7rHK9Htm+YDw6MynLK2YfsskL9rIc29h3Z/KdDrBxze43hkrhn2nGt0kGzicOq9ztyQvyHTK0PlJXpbk9Kxyqtga6//1Zu77Ku18f6YjMi9Pcpv5dXTF/P7ywxto463z8+2N+547mf6Rf+gGanluVulwSvLLSb68ZBvPXDEcu/AYvXqD++ZBmTpbPpnpyNl7M113cfgG2mi/ljL9s3uzNeadtKnHfSuePAaDYf1hxYfy5Ss+lI9csg0fyquvtx0fyjuWXP9g+lD+vhUfyreep2/oQ3le5zZJ7rHyMV/tw3qdNu7eaWOddu51IGvJdP779xyE++VA1PJdW1DLd3Wfc/Pyd8w3Tl+5baZ/Pn9ig23cqdvGGu0u9Y/qOm38yFzPj3fbatVxIDduMBimIfNh9APdxsFQy4oPZfvlIKgl0ykj/5rkJZlOjbnPwrxlT5FotzEv+5AtqKXdxhbul6trLe/bgudLq4152TMy/ZN6bqYLEV+T6VziNyT5vU228eqNtjG3c9Yqw+f3jW+gnbcujP9KpvM6z8h0GH/Xkm3sSPKrmU432ncdycuT/FqW7NG+UpubWclgMGztkE2c83pVtKEWtayx7FZ9q0WrDbWoZZO1tL5NZSvamJc/mL6R5fmZvqXjzplOOTphHn9ykhcsW8vicMh9sTkcqqrqvLVmZTqXclvaUItaNlpLpvNtP58kY4w98/evvqiqbj63tV1tqEUtG63lK2OMryb5QlV9cMw/RjHG+GJVfW0b20imrwJ7WKaLIP/7GOOdVfXFMcbrN9BGklyrqo7MdE1JjTH2zvX8W1V9Zck2fmCMcesV0y7J9P3K799gPUkOwV/KgUPYsUn+U6bz2RZVposntqsNtahlo7VcVlW3G2O8M0nGGJ+vqntn+iGDZb+QeSvaUItaNlrLf1TV9ccYX8h0kWiS6UvkM31l2Xa1kTHG15I8oar+Zv57WTaXww7PdJV2JRlVddwY42NVdViWD9uXV9X9krx4ritVda1MFwGufL9Yzma6NQ0Gw8aHbM23C7TbUItaNlHLVnyrRbsNtahlE7W0v01lK9pYY/0D9o0smU4jeEGST2T60Zj3z+MvWLaNlUPNDQMAcA1RVXdKMjJ9b/RtkvxQkveOMV62qfYESgCAa46qOiPTL3HtyPQre3dM8rok/2em76v9ow23KVACAFxzVNX5mX4x8DqZfsnrhDHGZ6vqeknOGWN830bbvNYW1wgAwMHtK2OMr47pQqNvunI9G7jQaJFACQBwzfIfVXX9eXzTV64vcsgbAOAapKquM8b491Wm3zjJcWOM8zfcpkAJAECHQ94AALQIlAAAtAiUAJtUVQ+tqguq6tKqetKBrgfgQBEoATbv1zN9EfDvbUVjVbWZ3/UFOOAESoBNqKq/TPLtSV6e5MiF6Tur6jVV9f+3d4coFURRHIf/Zx/iPhSLuxBMNotFcBNGLTYV0eIaTIJgdREWg8ko1zLBNuCZ5+PB96XLmXKm/WAY7ltVPVXV9sz8pqququo1yfk63gWgS1AC/MEY4zjJe5L9JJ+/Hl0muZ1umrhPcjEzT5KtJLtjjNOVLw6wAoISYFk7SR6m812SvZl5kjyOMb7/Zz2A5QlKgPX7WvcCAB2CEmBZL0kOpvNhkueZOcDG80chwLJOklxX1VmSjyRHM3OAjefqRQAAWnzyBgCgRVACANAiKAEAaBGUAAC0CEoAAFoEJQAALYISAIAWQQkAQMsP+0viqbp7iPoAAAAASUVORK5CYII=\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1152x1080 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "cE7foIo2h3Kj"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment/Assignment_2/201050_Tejas_A2.ipynb b/Assignment/Assignment_2/201050_Tejas_A2.ipynb
new file mode 100644
index 0000000..3eaf44c
--- /dev/null
+++ b/Assignment/Assignment_2/201050_Tejas_A2.ipynb
@@ -0,0 +1,926 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rvFM645NE-D2"
+      },
+      "source": [
+        "# Assignment 2\n",
+        "In this assignment, we will go through Perceptron, Linear Classifiers, Loss Functions, Gradient Descent and Back Propagation.\n",
+        "\n",
+        "\n",
+        "PS. this one is not from Stanford's course.\n",
+        "\n",
+        "\n",
+        "\n",
+        "\\\n",
+        "\n",
+        "## Instructions\n",
+        "* This notebook contain blocks of code, you are required to complete those blocks(where required)\n",
+        "* You are required to copy this notebook (\"copy to drive\" above) and complete the code.(DO NOT CHANGE THE NAME OF THE FUNCTIONS)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "QLtp15rqE-EU"
+      },
+      "source": [
+        "# Part 1: Perceptron\n",
+        "In this section, we will see how to implement a perceptron. Goal would be for you to delve into the mathematics.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zao4e-DphaGA"
+      },
+      "source": [
+        "## Intro\n",
+        "What's a perceptron? It's an algorithm modelled on biological computational model to classify things into binary classes. It's a supervides learning algorithm, meaning that you need to provide labelled data containing features and the actual classifications. A perceptron would take these features as input and spit out a binary value (0 or 1). While training the model with training data, we try to minimise the error and learn the parameters involved."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wDTUoAd6ixm-"
+      },
+      "source": [
+        "**How does it work?**\\\n",
+        "A perceptron is modelled on a biological neuron. A neuron has input dendrites and the output is carried by axons. Similarly, a perceptron takes inputs called \"features\". After processing, a perceptron gives output. For computation, it has a \"weight\" vector which is multipled with feature vector. An activation function is added to introduce some non linearities and the output is given out.\\\n",
+        "It can be represented as: $$  f=\\sum_{i=1}^{m} w_ix_i +b$$\n",
+        "\n",
+        "Let's implement this simple function to give an output.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "iXezofBIgzId"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "class perceptron():\n",
+        "    def __init__(self,num_input_features=8):\n",
+        "        self.weights = np.random.randn(num_input_features) #gives a row vector \n",
+        "        self.bias = np.random.random()\n",
+        "\n",
+        "    def activation(self,x):\n",
+        "        '''\n",
+        "            Implement heavside step activation function here (google ;))\n",
+        "        '''\n",
+        "        if(x>=0):\n",
+        "           return 1\n",
+        "        else:\n",
+        "           return 0\n",
+        "        pass\n",
+        "\n",
+        "    def forward(self,x: np.ndarray):\n",
+        "        '''\n",
+        "\n",
+        "            you have random initialized weights and bias\n",
+        "            you can access then using `self.weights` and `self.bias`\n",
+        "            you should use activation function before returning\n",
+        "        \n",
+        "            x : input features\n",
+        "            return : a binary value as the output of the perceptron \n",
+        "        '''\n",
+        "        # YOUR CODE HERE\n",
+        "        f= np.dot((self.weights).T, x) + self.bias\n",
+        "        t= self.activation(f)\n",
+        "        return t\n",
+        "        pass\n",
+        "        # YOUR CODE HERE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "oSKwDFAyocVo"
+      },
+      "outputs": [],
+      "source": [
+        "np.random.seed(0)\n",
+        "perc = perceptron(8) \n",
+        "# print(perc.forward(np.arange(8)))\n",
+        "assert perc.forward(np.arange(8))==1 #check what this does "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "NWTTg1e9r7uM"
+      },
+      "source": [
+        "# Part 2: Linear Classifier\n",
+        "In this section, we will see how to implement a linear Classifier.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DYDO4GcHr7uM"
+      },
+      "source": [
+        "## Intro\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-HFvjH06r7uN"
+      },
+      "source": [
+        "**How does it work?**\n",
+        "\n",
+        "Linear Classifier uses the following function: $$Y = WX+b$$ Where, $W$ is a 2d array of weights with shape (#features, #classes).\n",
+        "\n",
+        "\n",
+        "Let's implement this classifier.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "9A13CEkGr7uN"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "#classes= This is not a binary classification problem- I guess 5 classes is the default here- so we output a probability vector Y , shape (5,1), bias (5,1)\n",
+        "class LinearClassifier():\n",
+        "    def __init__(self,num_input_features=32,num_classes=5):\n",
+        "        self.weights = np.random.randn(num_input_features,num_classes) # (32,5) ( 32 features, if it was binary classification W would have been (32,1))\n",
+        "        self.bias = np.random.rand(num_classes)  # (1,5) => flexible if you give only one dimension-> b.shape is (5,)\n",
+        "\n",
+        "    def forward(self,x: np.ndarray):\n",
+        "        '''\n",
+        "            x: input features\n",
+        "            you have random initialized weights and bias\n",
+        "            you can access then using `self.weights` and `self.bias`\n",
+        "            return an output vector of num_classes size\n",
+        "        '''\n",
+        "        # YOUR CODE HERE \n",
+        "        Y= np.dot(x,self.weights)+self.bias # x is of shape (1,32) -> one value for each feature  , Y is (1,5) apparently \n",
+        "        # Y= np.dot( self.weights,x)+self.bias\n",
+        "        return Y\n",
+        "        pass\n",
+        "        # YOUR CODE HERE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zgzPxyTsr7uN",
+        "outputId": "c0868437-90ad-4f22-e8be-856673a53b24"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 1.30208164,  5.58136003,  0.87793013, -4.7332119 ,  4.81172123]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "\n",
+        "np.random.seed(0)\n",
+        "lc = LinearClassifier()\n",
+        "# print(lc)\n",
+        "(lc.forward(np.random.rand(1,32))) # passing a feature vector randomly of size (1,32)\n",
+        "# bias = np.random.rand(5) \n",
+        "# print(np.shape(bias))\n",
+        "# Should be close to:\n",
+        "# array([[ 1.30208164,  5.58136003,  0.87793013, -4.7332119 ,  4.81172123]])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "ZVgOVzJetuqo"
+      },
+      "source": [
+        "# Part 3: Loss Functions, Gradient descent and Backpropagation\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4pXryjpctuqy"
+      },
+      "source": [
+        "## Intro\n",
+        "\n",
+        "Loss Functions tells how \"off\" the output od our model is. Based upon the application, you can use several different loss functions. Formally, A loss function is a function $L:(z,y)\\in\\mathbb{R}\\times Y\\longmapsto L(z,y)\\in\\mathbb{R}$ that takes as inputs the predicted value $z$ corresponding to the real data value yy and outputs how different they are We'll implement L1 loss, L2 loss, Logistic loss, hinge loss and cross entropy loss functions."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QGRb8BHotuqy"
+      },
+      "source": [
+        "### **L1 loss**\n",
+        "L1 loss is the linear loss function  $L = \\dfrac{1}{2}(y−z) $\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "YxVh6IL2tuqz"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def L1Loss(z,y):\n",
+        "    '''\n",
+        "        y : True output.\n",
+        "        z : Predicted output.\n",
+        "        return : L\n",
+        "    '''\n",
+        "    L= 1/2*(y-z)\n",
+        "    return L\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2xy8ZS84cKtQ"
+      },
+      "source": [
+        "### **L2 loss**\n",
+        "L2 loss is the quadratic loss function or the least square error function  $L = \\dfrac{1}{2}(y−z)^2 $\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "JThp5P-KcKtS"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def L2Loss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    L= 1/2*(y-z)**2\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z2JNLnWYcLSC"
+      },
+      "source": [
+        "### **Hinge Loss**\n",
+        "Hinge loss is: $ L = max( 0, 1 - yz ) $"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "gQ1YM4J-cLSC"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def hingeLoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    t= max(0, 1-y*z)\n",
+        "    return t\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m15_MjradMNY"
+      },
+      "source": [
+        "### **Cross Entropy Loss**\n",
+        "Another very famous loss function is Cross Entropy loss: $ L = −[ylog(z)+(1−y)log(1−z)] $."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "snJLqhszdMNY"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np # We used cross entropy loss in binary classification problems- if y is 1 and z is close to zero/ vice versa there is huge loss \n",
+        "def CELoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    L= -1*(y*np.log(z)+(1-y)*np.log(1-z))\n",
+        "    return L\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OsRPsfzxyEVL"
+      },
+      "source": [
+        "### **0-1 Loss**\n",
+        "Loss Function used by perceptron is: $ \\begin{cases} \n",
+        "      0=z-y & z=y \\\\\n",
+        "      1=\\dfrac{z-y}{z-y} & z\\neq y\n",
+        "   \\end{cases} $."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "5sA7GxLHyEVM"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "def zeroOneLoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    # essentially it seems loss is 1 if your prediction is not completely accurate?\n",
+        "    if(z==y):\n",
+        "       return 0\n",
+        "    else:\n",
+        "      return 1\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CWhbibHcgRR8"
+      },
+      "source": [
+        "## Cost Function\n",
+        "The cost function $J$ is commonly used to assess the performance of a model, and is defined with the loss function $L$ as follows:\n",
+        "$$\\boxed{J(\\theta)=\\sum_{i=1}^mL(h_\\theta(x^{(i)}), y^{(i)})}$$\n",
+        "where $h_\\theta$ is the hypothesis function i.e. the function used to predict the output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "SSbmhW4og97t"
+      },
+      "outputs": [],
+      "source": [
+        "lossFunctions = {\n",
+        "    \"l1\" : L1Loss,\n",
+        "    \"l2\" : L2Loss,\n",
+        "    \"hinge\" : hingeLoss,\n",
+        "    \"cross-entropy\" : CELoss,\n",
+        "    \"0-1\" : zeroOneLoss\n",
+        "} #dictionary \n",
+        "\n",
+        "def cost(Z : np.ndarray, Y : np.ndarray, loss : str):\n",
+        "    '''\n",
+        "        Z : a numpy array of predictions.\n",
+        "        Y : a numpy array of true values.\n",
+        "        return : A numpy array of costs calculated for each example.\n",
+        "    '''\n",
+        "    loss_func = lossFunctions[loss]\n",
+        "    # YOUR CODE HERE\n",
+        "    J = None\n",
+        "    for i in range(len(Y)):\n",
+        "      J+=loss_func(Z[i],Y[i])\n",
+        "    # YOUR CODE HERE\n",
+        "    return J\n",
+        "    pass"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "upsN7A0zjGqx"
+      },
+      "source": [
+        "## Gradient Descent and Back Propagation\n",
+        "Gradient Descent is an algorithm that minimizes the loss function by calculating it's gradient. By noting $\\alpha\\in\\mathbb{R}$ the learning rate, the update rule for gradient descent is expressed with the learning rate $\\alpha$ and the cost function $J$ as follows:\n",
+        "\n",
+        "$$\\boxed{ W \\longleftarrow W -\\alpha\\nabla J( W )}$$\n",
+        "​\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AFCN-fYCqidi"
+      },
+      "source": [
+        "But we need to find the partial derivative of Loss function wrt every parameter to know what is the slight change that we need to apply to our parameters. This becomes particularly hard if we have more than 1 layer in our algorithm. Here's where **Back Propagation** comes in. It's a way to find gradients wrt every parameter using the chain rule. Backpropagation is a method to update the weights in the neural network by taking into account the actual output and the desired output. The derivative with respect to weight ww is computed using chain rule and is of the following form:\n",
+        "\n",
+        "$$\\boxed{\\frac{\\partial L(z,y)}{\\partial w}=\\frac{\\partial L(z,y)}{\\partial a}\\times\\frac{\\partial a}{\\partial z}\\times\\frac{\\partial z}{\\partial w}}$$\n",
+        "​\n",
+        " \n",
+        "As a result, the weight is updated as follows:\n",
+        "\n",
+        "$$\\boxed{w\\longleftarrow w-\\alpha\\frac{\\partial L(z,y)}{\\partial w}}$$\n",
+        "\n",
+        "So, In a neural network, weights are updated as follows:\n",
+        "\n",
+        "* Step 1: Take a batch of training data.\n",
+        "* Step 2: Perform forward propagation to obtain the corresponding loss.\n",
+        "* Step 3: Backpropagate the loss to get the gradients.\n",
+        "* Step 4: Use the gradients to update the weights of the network.\n",
+        "​\n",
+        "\n",
+        "Bonus Problem\n",
+        " \n",
+        "Now, Assuming that you know Back Propagation (read a bit about it, if you don't), we'll now implement an image classification model on CIFAR-10."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sJoG5kkYopRN"
+      },
+      "source": [
+        "# **Bonus Problem**\n",
+        "\n",
+        "Now, Assuming that you know Back Propagation (read a bit about it, if you don't), we'll now implement an image classification model on CIFAR-10."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_4-4RceVsor_",
+        "outputId": "1b70e75f-b529-475d-9d48-489272c9502d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.8.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "import tensorflow as tf  \n",
+        " \n",
+        "# Display the version\n",
+        "print(tf.__version__)    \n",
+        " \n",
+        "# other imports\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from tensorflow.keras.layers import Input, Conv2D, Dense, Flatten, Dropout\n",
+        "from tensorflow.keras.layers import GlobalMaxPooling2D, MaxPooling2D\n",
+        "from tensorflow.keras.layers import BatchNormalization\n",
+        "from tensorflow.keras.models import Model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yyplk5PLEUsJ",
+        "outputId": "2ea88acf-7d99-4b3a-ad1f-5e52c49256fd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "170500096/170498071 [==============================] - 12s 0us/step\n",
+            "170508288/170498071 [==============================] - 12s 0us/step\n",
+            "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Load in the data\n",
+        "cifar10 = tf.keras.datasets.cifar10 \n",
+        "#The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. \n",
+        " \n",
+        "# Distribute it to train and test set\n",
+        "(x_train, y_train), (x_test, y_test) = cifar10.load_data() \n",
+        "# x is (no_of_images, image size in pixels(32,32), 3( red, blue, green)) => y is the corresponding classification into one of the ten classes ?\n",
+        "print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n",
+        "\n",
+        "# Reduce pixel values\n",
+        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
+        " \n",
+        "# flatten the label values\n",
+        "y_train, y_test = y_train.flatten(), y_test.flatten()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 309
+        },
+        "id": "a4gbmOEdfyKD",
+        "outputId": "1c7d8383-8c14-4c4b-b9a2-7f30c6d0958e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 9 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "\n",
+        "'''visualize data by plotting images''' \n",
+        "import matplotlib.pyplot as plt\n",
+        "key= {0:'airplane', 1:'automobile', 2:'bird',\t3:'cat',4:'deer',\t\t5:'dog',6:'frog',\t7:'horse',8:'ship',\t9:'truck'}\n",
+        "# YOUR CODE HERE\n",
+        "f, axarr = plt.subplots(3,3)\n",
+        "f.tight_layout()\n",
+        "for i in range(0,3):\n",
+        "  for j in range(0,3):\n",
+        "    axarr[i][j].imshow(x_train[3*(i)+j])\n",
+        "    axarr[i][j].set_title(key[y_train[3*i+j]])\n",
+        "# YOUR CODE HERE"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "yJgho2AEBFbx",
+        "outputId": "d862bc4b-7c9e-491c-9947-ace29e5a9e8a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "number of classes: 10\n",
+            "Model: \"model\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " input_1 (InputLayer)        [(None, 32, 32, 3)]       0         \n",
+            "                                                                 \n",
+            " conv2d (Conv2D)             (None, 30, 30, 32)        896       \n",
+            "                                                                 \n",
+            " max_pooling2d (MaxPooling2D  (None, 15, 15, 32)       0         \n",
+            " )                                                               \n",
+            "                                                                 \n",
+            " conv2d_1 (Conv2D)           (None, 13, 13, 64)        18496     \n",
+            "                                                                 \n",
+            " max_pooling2d_1 (MaxPooling  (None, 6, 6, 64)         0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " flatten (Flatten)           (None, 2304)              0         \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 10)                23050     \n",
+            "                                                                 \n",
+            " dense_1 (Dense)             (None, 10)                110       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 42,552\n",
+            "Trainable params: 42,552\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ],
+      "source": [
+        "()\n",
+        "# number of classes\n",
+        "K = len(set(y_train)) #set() method is used to convert any of the iterable to sequence of iterable elements with distinct elements\n",
+        "#set(y_train) will most liekly be the set s={0,1,..9}\n",
+        "'''\n",
+        " calculate total number of classes\n",
+        " for output layer\n",
+        "'''\n",
+        "print(\"number of classes:\", K)\n",
+        "''' \n",
+        " Build the model using the functional API\n",
+        " input layer\n",
+        "'''\n",
+        "'''\n",
+        "  YOUR CODE HERE\n",
+        "'''\n",
+        " \n",
+        "'''Hidden layer'''\n",
+        "# YOUR CODE HERE\n",
+        "pass\n",
+        "visible = Input(shape=x_train[0].shape) # (32,32,3)\n",
+        "conv1 = Conv2D(32, (3,3), activation='relu')(visible)\n",
+        "pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
+        "conv2 = Conv2D(64,(3,3),activation='relu')(pool1)\n",
+        "pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
+        "conv3 = Conv2D(128,(3,3),activation='relu')(pool1)\n",
+        "pool3 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
+        "flat = Flatten()(pool2)\n",
+        "hidden1 = Dense(10, activation='relu')(flat)\n",
+        "\n",
+        "# YOUR CODE HERE\n",
+        " \n",
+        "\"\"\"last hidden layer i.e.. output layer\"\"\"\n",
+        "# YOUR CODE HERE\n",
+        "output = Dense(10, activation='softmax')(hidden1)\n",
+        "model = Model(inputs=visible, outputs=output)\n",
+        "pass\n",
+        "# YOUR CODE HERE\n",
+        " \n",
+        "'''model description'''\n",
+        "model.summary()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "PLc4Bay65TyA"
+      },
+      "outputs": [],
+      "source": [
+        "# Compile\n",
+        "'''\n",
+        "  YOUR CODE HERE\n",
+        "'''\n",
+        "model.compile(optimizer='adam',\n",
+        "              loss='sparse_categorical_crossentropy',\n",
+        "              metrics=['accuracy'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "U0fGsDCRsQrn",
+        "outputId": "dba01b2c-438d-40c8-8b23-d2ed47348c6b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/50\n",
+            "1563/1563 [==============================] - 66s 42ms/step - loss: 1.7323 - accuracy: 0.3531 - val_loss: 1.4692 - val_accuracy: 0.4593\n",
+            "Epoch 2/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 1.4196 - accuracy: 0.4759 - val_loss: 1.3474 - val_accuracy: 0.5011\n",
+            "Epoch 3/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 1.3043 - accuracy: 0.5190 - val_loss: 1.2650 - val_accuracy: 0.5349\n",
+            "Epoch 4/50\n",
+            "1563/1563 [==============================] - 63s 41ms/step - loss: 1.2301 - accuracy: 0.5452 - val_loss: 1.2137 - val_accuracy: 0.5570\n",
+            "Epoch 5/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 1.1710 - accuracy: 0.5705 - val_loss: 1.1862 - val_accuracy: 0.5702\n",
+            "Epoch 6/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 1.1214 - accuracy: 0.5873 - val_loss: 1.1861 - val_accuracy: 0.5733\n",
+            "Epoch 7/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 1.0811 - accuracy: 0.6043 - val_loss: 1.1200 - val_accuracy: 0.5905\n",
+            "Epoch 8/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 1.0517 - accuracy: 0.6149 - val_loss: 1.0898 - val_accuracy: 0.6027\n",
+            "Epoch 9/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 1.0182 - accuracy: 0.6263 - val_loss: 1.0966 - val_accuracy: 0.6031\n",
+            "Epoch 10/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.9975 - accuracy: 0.6360 - val_loss: 1.0835 - val_accuracy: 0.6082\n",
+            "Epoch 11/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.9774 - accuracy: 0.6430 - val_loss: 1.0664 - val_accuracy: 0.6211\n",
+            "Epoch 12/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.9573 - accuracy: 0.6501 - val_loss: 1.0489 - val_accuracy: 0.6255\n",
+            "Epoch 13/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.9350 - accuracy: 0.6576 - val_loss: 1.0746 - val_accuracy: 0.6233\n",
+            "Epoch 14/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.9212 - accuracy: 0.6622 - val_loss: 1.0336 - val_accuracy: 0.6384\n",
+            "Epoch 15/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.9069 - accuracy: 0.6675 - val_loss: 1.0467 - val_accuracy: 0.6305\n",
+            "Epoch 16/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.8918 - accuracy: 0.6744 - val_loss: 1.0304 - val_accuracy: 0.6312\n",
+            "Epoch 17/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.8801 - accuracy: 0.6791 - val_loss: 1.0254 - val_accuracy: 0.6370\n",
+            "Epoch 18/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.8679 - accuracy: 0.6831 - val_loss: 1.0184 - val_accuracy: 0.6392\n",
+            "Epoch 19/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.8544 - accuracy: 0.6868 - val_loss: 1.1225 - val_accuracy: 0.6222\n",
+            "Epoch 20/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.8466 - accuracy: 0.6914 - val_loss: 1.0388 - val_accuracy: 0.6328\n",
+            "Epoch 21/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.8297 - accuracy: 0.6958 - val_loss: 1.0181 - val_accuracy: 0.6418\n",
+            "Epoch 22/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.8222 - accuracy: 0.6987 - val_loss: 1.0307 - val_accuracy: 0.6401\n",
+            "Epoch 23/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.8180 - accuracy: 0.7007 - val_loss: 1.0310 - val_accuracy: 0.6439\n",
+            "Epoch 24/50\n",
+            "1563/1563 [==============================] - 62s 40ms/step - loss: 0.8049 - accuracy: 0.7052 - val_loss: 1.0461 - val_accuracy: 0.6341\n",
+            "Epoch 25/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7983 - accuracy: 0.7070 - val_loss: 1.0432 - val_accuracy: 0.6411\n",
+            "Epoch 26/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7916 - accuracy: 0.7116 - val_loss: 1.0347 - val_accuracy: 0.6439\n",
+            "Epoch 27/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7816 - accuracy: 0.7135 - val_loss: 1.0814 - val_accuracy: 0.6300\n",
+            "Epoch 28/50\n",
+            "1563/1563 [==============================] - 63s 41ms/step - loss: 0.7763 - accuracy: 0.7178 - val_loss: 1.0354 - val_accuracy: 0.6500\n",
+            "Epoch 29/50\n",
+            "1563/1563 [==============================] - 63s 41ms/step - loss: 0.7692 - accuracy: 0.7182 - val_loss: 1.0428 - val_accuracy: 0.6473\n",
+            "Epoch 30/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.7603 - accuracy: 0.7201 - val_loss: 1.0590 - val_accuracy: 0.6435\n",
+            "Epoch 31/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.7528 - accuracy: 0.7243 - val_loss: 1.0498 - val_accuracy: 0.6509\n",
+            "Epoch 32/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.7473 - accuracy: 0.7252 - val_loss: 1.0959 - val_accuracy: 0.6316\n",
+            "Epoch 33/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.7462 - accuracy: 0.7249 - val_loss: 1.0841 - val_accuracy: 0.6359\n",
+            "Epoch 34/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7369 - accuracy: 0.7289 - val_loss: 1.0814 - val_accuracy: 0.6388\n",
+            "Epoch 35/50\n",
+            "1563/1563 [==============================] - 63s 41ms/step - loss: 0.7287 - accuracy: 0.7315 - val_loss: 1.0593 - val_accuracy: 0.6459\n",
+            "Epoch 36/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7255 - accuracy: 0.7350 - val_loss: 1.0535 - val_accuracy: 0.6532\n",
+            "Epoch 37/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7183 - accuracy: 0.7357 - val_loss: 1.0714 - val_accuracy: 0.6474\n",
+            "Epoch 38/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7138 - accuracy: 0.7381 - val_loss: 1.0699 - val_accuracy: 0.6470\n",
+            "Epoch 39/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7058 - accuracy: 0.7393 - val_loss: 1.0864 - val_accuracy: 0.6468\n",
+            "Epoch 40/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.7033 - accuracy: 0.7404 - val_loss: 1.0904 - val_accuracy: 0.6413\n",
+            "Epoch 41/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.6999 - accuracy: 0.7420 - val_loss: 1.1323 - val_accuracy: 0.6451\n",
+            "Epoch 42/50\n",
+            "1563/1563 [==============================] - 63s 40ms/step - loss: 0.6904 - accuracy: 0.7440 - val_loss: 1.0680 - val_accuracy: 0.6539\n",
+            "Epoch 43/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6854 - accuracy: 0.7448 - val_loss: 1.0869 - val_accuracy: 0.6483\n",
+            "Epoch 44/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6857 - accuracy: 0.7465 - val_loss: 1.1113 - val_accuracy: 0.6442\n",
+            "Epoch 45/50\n",
+            "1563/1563 [==============================] - 63s 41ms/step - loss: 0.6802 - accuracy: 0.7480 - val_loss: 1.0838 - val_accuracy: 0.6474\n",
+            "Epoch 46/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6781 - accuracy: 0.7507 - val_loss: 1.1039 - val_accuracy: 0.6472\n",
+            "Epoch 47/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6673 - accuracy: 0.7518 - val_loss: 1.1137 - val_accuracy: 0.6500\n",
+            "Epoch 48/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6655 - accuracy: 0.7544 - val_loss: 1.1335 - val_accuracy: 0.6410\n",
+            "Epoch 49/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6588 - accuracy: 0.7549 - val_loss: 1.1107 - val_accuracy: 0.6471\n",
+            "Epoch 50/50\n",
+            "1563/1563 [==============================] - 64s 41ms/step - loss: 0.6582 - accuracy: 0.7560 - val_loss: 1.1022 - val_accuracy: 0.6497\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Fit\n",
+        "'''\n",
+        "  YOUR CODE HERE\n",
+        "'''\n",
+        "r = model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=50)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "RDq_RE6osSh8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 283
+        },
+        "outputId": "6015ee91-cae4-48a6-ae5f-357dc16e6636"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original label is cat and predicted label is cat\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ],
+      "source": [
+        "# label mapping\n",
+        " \n",
+        "labels = '''airplane automobile bird cat deer dog frog horse ship truck'''.split()\n",
+        " \n",
+        "# select the image from our test dataset\n",
+        "image_number = 0\n",
+        " \n",
+        "# display the image\n",
+        "plt.imshow(x_test[image_number])\n",
+        " \n",
+        "# load the image in an array\n",
+        "n = np.array(x_test[image_number])\n",
+        " \n",
+        "# reshape it\n",
+        "p = n.reshape(1, 32, 32, 3)\n",
+        " \n",
+        "# pass in the network for prediction and\n",
+        "# save the predicted label\n",
+        "predicted_label = labels[model.predict(p).argmax()]\n",
+        " \n",
+        "# load the original label\n",
+        "original_label = labels[y_test[image_number]]\n",
+        " \n",
+        "# display the result\n",
+        "print(\"Original label is {} and predicted label is {}\".format(\n",
+        "    original_label, predicted_label))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        ""
+      ],
+      "metadata": {
+        "id": "5VIj4PyVxG7G"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "201050_Tejas_A2.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Assignment/Assignment_3/201050_Tejas_A3.ipynb b/Assignment/Assignment_3/201050_Tejas_A3.ipynb
new file mode 100644
index 0000000..757a0b2
--- /dev/null
+++ b/Assignment/Assignment_3/201050_Tejas_A3.ipynb
@@ -0,0 +1,405 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RNN for image classification on MNIST dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importing libraries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt \n",
+    "import torchvision\n",
+    "from torchvision import datasets\n",
+    "from torchvision.transforms import ToTensor\n",
+    "from torchvision.transforms import transforms \n",
+    "import torch.nn as nn \n",
+    "from torch import optim,utils\n",
+    "import os\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loading the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset MNIST\n",
+      "    Number of datapoints: 60000\n",
+      "    Root location: /Users/tejasr/Documents/IITK/Semesters/sem 4 /Stamatics project /data\n",
+      "    Split: Train\n",
+      "    StandardTransform\n",
+      "Transform: Compose(\n",
+      "               ToTensor()\n",
+      "           )\n",
+      "Dataset MNIST\n",
+      "    Number of datapoints: 10000\n",
+      "    Root location: /Users/tejasr/Documents/IITK/Semesters/sem 4 /Stamatics project /data\n",
+      "    Split: Test\n",
+      "    StandardTransform\n",
+      "Transform: Compose(\n",
+      "               ToTensor()\n",
+      "           )\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "transform = transforms.Compose([transforms.ToTensor()])\n",
+    "\n",
+    "train_data= datasets.MNIST(train=True,root='/Users/tejasr/Documents/IITK/Semesters/sem 4 /Stamatics project /data',download = True,\n",
+    "                           transform=transform)\n",
+    "test_data= datasets.MNIST(train=False,root='/Users/tejasr/Documents/IITK/Semesters/sem 4 /Stamatics project /data',download = True,transform=transform)\n",
+    "print(train_data)\n",
+    "print(test_data)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "batch_size=100\n",
+    "\n",
+    "trainLoader= torch.utils.data.DataLoader(dataset= train_data, batch_size=batch_size, shuffle=True)\n",
+    "testLoader= torch.utils.data.DataLoader(dataset=test_data, batch_size=batch_size, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Data Visualisation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Image tensor is of size torch.Size([100, 1, 28, 28]) \n",
+      "Labels tensor is of size torch.Size([100])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Exploring the dataset\n",
+    "\n",
+    "# functions to show an image\n",
+    "def imshow(img):\n",
+    "    npimg = img.numpy()\n",
+    "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
+    "\n",
+    "# get some random training images\n",
+    "dataiter = iter(trainLoader)\n",
+    "images, labels = dataiter.next()  #returns iterator and shifts pointer ahead to next position/image \n",
+    "images_shape= images.shape\n",
+    "labels_shape= labels.shape\n",
+    "print(\"Image tensor is of size\", images_shape,\"\\nLabels tensor is of size\", labels_shape)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The image tensor is of size [100,1,28,28], meaning that each batch has 100 images, and each image is of size 28*28 pixels, and the corresponding labels size is 100, giving us the number classification of each digit. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "numpy.squeeze() removes single dimensional entities from the array, here a [1,28,28] image is converted into a [28,28] image which can be plotted "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 25 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure = plt.figure()\n",
+    "for index in range(1, 26):\n",
+    "    plt.subplot(5, 5, index)\n",
+    "    plt.axis('off')\n",
+    "    plt.imshow(images[index-1].numpy().squeeze(), cmap='inferno')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "steps = 28\n",
+    "input_size = 28 # represents the size of the input at each time unit\n",
+    "hidden_size =60\n",
+    "output_size = 10 # equal to the number of classes= 10. \n",
+    "n_epochs = 30\n",
+    "num_layers=1 #default, number of stacked LSTM layers\n",
+    "learning_rate=0.0001"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class RNN(nn.Module):\n",
+    "    def __init__(self, input_size, hidden_size, output_size, num_layers):\n",
+    "        super(RNN,self).__init__()\n",
+    "\n",
+    "        self.input_size=input_size\n",
+    "        self.hidden_size=hidden_size\n",
+    "        self.output_size=output_size\n",
+    "        self.num_layers=num_layers\n",
+    "        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
+    "        # self.rnn= nn.RNN(self.input_size,self.hidden_size) #building an lstm\n",
+    "        self.fc= nn.Linear(self.hidden_size,self.output_size)\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        h0= torch.zeros(self.num_layers, x.size(0), self.hidden_size )#Initial hidden state \n",
+    "        c0= torch.zeros(self.num_layers, x.size(0), self.hidden_size ) #Initial cell state \n",
+    "        #The lstm model requires these two input along with x \n",
+    "        out,lstm= self.lstm(x,(h0,c0))\n",
+    "        out= self.fc(out[:,-1,:])\n",
+    "        return out \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RNN(\n",
+      "  (lstm): LSTM(28, 60, batch_first=True)\n",
+      "  (fc): Linear(in_features=60, out_features=10, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# nn.Module[input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, output_size=output_size,steps=steps,batch_size=batch_size]\n",
+    "model = RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, output_size=output_size)\n",
+    "print(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining the loss and the accuracy function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "criterion= nn.CrossEntropyLoss() #We use cross entropy loss \n",
+    "optimizer= torch.optim.Adam(model.parameters(), lr=learning_rate) #Adam is Adaptative Moment Estimation, improved SGD "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def accuracy(testLoader, model): #calculates the accuracy on the test dataset \n",
+    "    correct=0\n",
+    "    total=0\n",
+    "    for images,labels in testLoader:\n",
+    "        images = images.reshape(-1, 28, input_size)\n",
+    "        output= model(images) #size of output.data.size() is [100,10]-> for each image in the batch we need to take the maximum along the rows\n",
+    "        #  print(output.data.size(),\" \",labels.size())\n",
+    "        #labels are of size [100] as expected \n",
+    "        #torch.max returns a tuple, max_values, and max_indices \n",
+    "        _, predictions = torch.max(output.data, 1)\n",
+    "        #Here, the max indices corresponds to the value of the predicted digit, if index 7 has the max value then we predict 7 and so on \n",
+    "        assert(len(predictions)==len(labels))\n",
+    "        for i in range(0,len(predictions)):\n",
+    "            if(predictions[i]==labels[i]):\n",
+    "                correct+=1\n",
+    "        total+=len(predictions)    \n",
+    "    return correct/total\n",
+    "    # print(f\"Accuracy is {correct/total:.4f} on the test dataset\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Finally, training the model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We iterate over a total of 30 epochs and train the model\n",
+      "Loss in epoch 2 of 30 is: 0.7302, with an accuracy of 0.8199 on the test dataset\n",
+      "Loss in epoch 4 of 30 is: 0.3852, with an accuracy of 0.8955 on the test dataset\n",
+      "Loss in epoch 6 of 30 is: 0.3641, with an accuracy of 0.9145 on the test dataset\n",
+      "Loss in epoch 8 of 30 is: 0.1878, with an accuracy of 0.9292 on the test dataset\n",
+      "Loss in epoch 10 of 30 is: 0.2532, with an accuracy of 0.9372 on the test dataset\n",
+      "Loss in epoch 12 of 30 is: 0.3514, with an accuracy of 0.9427 on the test dataset\n",
+      "Loss in epoch 14 of 30 is: 0.2539, with an accuracy of 0.9459 on the test dataset\n",
+      "Loss in epoch 16 of 30 is: 0.0651, with an accuracy of 0.9518 on the test dataset\n",
+      "Loss in epoch 18 of 30 is: 0.2226, with an accuracy of 0.9513 on the test dataset\n",
+      "Loss in epoch 20 of 30 is: 0.1403, with an accuracy of 0.9514 on the test dataset\n",
+      "Loss in epoch 22 of 30 is: 0.0808, with an accuracy of 0.9604 on the test dataset\n",
+      "Loss in epoch 24 of 30 is: 0.1253, with an accuracy of 0.9597 on the test dataset\n",
+      "Loss in epoch 26 of 30 is: 0.1497, with an accuracy of 0.9623 on the test dataset\n",
+      "Loss in epoch 28 of 30 is: 0.1490, with an accuracy of 0.9647 on the test dataset\n",
+      "Loss in epoch 30 of 30 is: 0.1882, with an accuracy of 0.9648 on the test dataset\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"We iterate over a total of {n_epochs} epochs and train the model\")\n",
+    "for epoch in range(0,n_epochs):\n",
+    "    for i, (images,labels) in enumerate(trainLoader):\n",
+    "        optimizer.zero_grad()\n",
+    "        images = images.reshape(-1, 28, input_size)\n",
+    "        output= model(images)\n",
+    "        loss= criterion(output, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "    if(epoch%2):\n",
+    "        print(f\"Loss in epoch {epoch+1} of {n_epochs} is: {loss.item():.4f}, with an accuracy of {accuracy(testLoader, model):.4f} on the test dataset\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Final accuracy with the trained model is 0.9648 on the test dataset\n"
+     ]
+    }
+   ],
+   "source": [
+    "correct=0\n",
+    "total=0\n",
+    "\n",
+    "for images,labels in testLoader:\n",
+    "     images = images.reshape(-1, 28, input_size)\n",
+    "     output= model(images) #size of output.data.size() is [100,10]-> for each image in the batch we need to take the maximum along the rows\n",
+    "    #  print(output.data.size(),\" \",labels.size())\n",
+    "    #labels are of size [100] as expected \n",
+    "    #torch.max returns a tuple, max_values, and max_indices \n",
+    "     _, predictions = torch.max(output.data, 1)\n",
+    "     #Here, the max indices corresponds to the value of the predicted digit, if index 7 has the max value then we predict 7 and so on \n",
+    "     assert(len(predictions)==len(labels))\n",
+    "     for i in range(0,len(predictions)):\n",
+    "         if(predictions[i]==labels[i]):\n",
+    "             correct+=1\n",
+    "     total+=len(predictions)    \n",
+    "\n",
+    "print(f\"Final accuracy with the trained model is {correct/total:.4f} on the test dataset\")\n",
+    "     \n",
+    "     "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### We get a final accuracy of 96.5% with the help of the RNN(LSTM) model!\n",
+    "##### The model gets higher accuracies much lesser number of epochs with around hundred hidden layers, but that model reaches almost peak accuracy in one epoch. "
+   ]
+  }
+ ],
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