diff --git a/Assignment/Assignment3/210551_Kushagra_A3.ipynb b/Assignment/Assignment3/210551_Kushagra_A3.ipynb
new file mode 100644
index 0000000..996d081
--- /dev/null
+++ b/Assignment/Assignment3/210551_Kushagra_A3.ipynb
@@ -0,0 +1,382 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "2105551_Kushagra_A3.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "id": "qTYZDsFvM_8B"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "import keras\n",
+        "import pandas as pd\n",
+        "import sklearn \n",
+        "import tensorflow.keras.layers\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
+        "x_train, x_test = x_train/255.0, x_test/255.0"
+      ],
+      "metadata": {
+        "id": "7Z0T6nmRcWHA"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "fig, ax = plt.subplots(5, 6, figsize = (6,5))\n",
+        "for i, ax in enumerate(ax.flatten()):\n",
+        "    ax.axis('off')\n",
+        "    ax.set_title(f'label : {y_train[i]}')\n",
+        "    ax.imshow(x_train[i], cmap = 'binary')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 319
+        },
+        "id": "aNpglB0YcWUg",
+        "outputId": "f064909b-d524-48c7-e8d7-15960714eb6a"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 432x360 with 30 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from tensorflow.keras import layers"
+      ],
+      "metadata": {
+        "id": "jIwiWmI6cWZ9"
+      },
+      "execution_count": 23,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "x_train=x_train/255\n",
+        "x_test=x_test/255\n",
+        "x_train_flattened=x_train.reshape(len(x_train),28*28)\n",
+        "x_test_flattened=x_test.reshape(len(x_test),28*28)\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "zbde3xEjcWdW"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(x_train_flattened.shape)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "en9gtiGWcWhg",
+        "outputId": "e744a5f8-6c69-4a78-8a76-cbfa5c37169b"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(60000, 784)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "input = tensorflow.keras.layers.Input(shape = (28,28))\n",
+        "x = tensorflow.keras.layers.LSTM(64, activation = \"relu\")(input)\n",
+        "out = tensorflow.keras.layers.Dense(10, activation = \"softmax\")(x)\n",
+        "\n",
+        "model = tf.keras.models.Model(input, out)\n",
+        "model.summary()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j8w-nrKad5gV",
+        "outputId": "324a560b-b91f-4135-d5b4-b510302e0097"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Model: \"model\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " input_1 (InputLayer)        [(None, 28, 28)]          0         \n",
+            "                                                                 \n",
+            " lstm (LSTM)                 (None, 64)                23808     \n",
+            "                                                                 \n",
+            " dense (Dense)               (None, 10)                650       \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 24,458\n",
+            "Trainable params: 24,458\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.compile(optimizer  =\"adam\", loss = \"sparse_categorical_crossentropy\", metrics = [\"acc\"])\n",
+        "fitted_model = model.fit(x_train, y_train, epochs = 8)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vWlJ4WE-d5jW",
+        "outputId": "7539edf7-3e8a-4921-c3c0-5bb1c9d42904"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/8\n",
+            "1875/1875 [==============================] - 23s 12ms/step - loss: 1.1438 - acc: 0.5772\n",
+            "Epoch 2/8\n",
+            "1875/1875 [==============================] - 24s 13ms/step - loss: 0.9362 - acc: 0.6644\n",
+            "Epoch 3/8\n",
+            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.7276 - acc: 0.7556\n",
+            "Epoch 4/8\n",
+            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.6240 - acc: 0.7926\n",
+            "Epoch 5/8\n",
+            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.5519 - acc: 0.8176\n",
+            "Epoch 6/8\n",
+            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.4625 - acc: 0.8461\n",
+            "Epoch 7/8\n",
+            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.3996 - acc: 0.8686\n",
+            "Epoch 8/8\n",
+            "1875/1875 [==============================] - 22s 12ms/step - loss: 0.3628 - acc: 0.8828\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "loss, accuracy = model.evaluate(x_test, y_test)\n",
+        "print(\"Loss: {0}, Accuracy: {1}\".format(loss, accuracy))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "eYHdr4Dmfdtb",
+        "outputId": "cbace559-668d-46f2-9601-5b9e4fd052af"
+      },
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 2s 5ms/step - loss: 0.3184 - acc: 0.8898\n",
+            "Loss: 0.3184027075767517, Accuracy: 0.8898000121116638\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "accuracy = fitted_model.history['acc']\n",
+        "epochs = range(0, 8)\n",
+        "\n",
+        "plt.title('Training accuracy')\n",
+        "plt.xlabel('Epochs')\n",
+        "plt.ylabel('Accuracy')\n",
+        "plt.plot(epochs, accuracy)\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "id": "Lhj2PDHad5c8",
+        "outputId": "7db67af2-8ebf-4b36-d191-85c527343c8f"
+      },
+      "execution_count": 37,
+      "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"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_pred = model.predict(x_test)\n",
+        "\n",
+        "fig, ax = plt.subplots(5, 6, figsize = (15,10))\n",
+        "for i, ax in enumerate(ax.flatten()):\n",
+        "    ax.axis('off')\n",
+        "    ax.set_title(f'Predicted number : {y_pred[i].argmax()}\\nActual number : {y_test[i]}')\n",
+        "    ax.imshow(x_test[i], cmap ='binary')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 606
+        },
+        "id": "b_oMVcHgd5YA",
+        "outputId": "4bc699aa-e128-4ca8-b1e2-086620caad69"
+      },
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1080x720 with 30 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.evaluate(x_test,y_test)\n",
+        "predicted_y=model.predict(x_test)"
+      ],
+      "metadata": {
+        "id": "IAohY0LWj_P8",
+        "outputId": "73bca0c9-9375-4492-ab34-b1c53f313cf1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "313/313 [==============================] - 2s 7ms/step - loss: 0.3184 - acc: 0.8898\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_predicted=[]\n",
+        "for i in range(0,len(predicted_y)) :\n",
+        "    y_predicted.append(np.argmax(predicted_y[i]))\n"
+      ],
+      "metadata": {
+        "id": "EriilvW6j3SU"
+      },
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm=tf.math.confusion_matrix(labels=y_test,predictions=y_predicted)\n",
+        "plt.figure(figsize=(10,7))\n",
+        "sn.heatmap(cm,annot=True,fmt='d')\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.xlabel(\"Predicted\")\n",
+        "plt.ylabel(\"Truth\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 458
+        },
+        "id": "xSr199Iid5Ju",
+        "outputId": "284e3fe1-0db3-4e29-ec0f-4b86c1bbc015"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 720x504 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {
+            "needs_background": "light"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/Assignment/Assignment_1/210551_Kushagra_1a(numpy).ipynb b/Assignment/Assignment_1/210551_Kushagra_1a(numpy).ipynb
new file mode 100644
index 0000000..8a04843
--- /dev/null
+++ b/Assignment/Assignment_1/210551_Kushagra_1a(numpy).ipynb
@@ -0,0 +1,913 @@
+{
+  "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": "Copy of DL_Stamatics_A1.ipynb",
+      "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.(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": 1,
+      "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",
+        "outputId": "cbdfbb9e-40d5-4427-8f65-6ad67c8fa6ec",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "source": [
+        "### YOUR CODE HERE\n",
+        "\n",
+        "M =np.array([[1,2,3], [4,5,7], [7,8,9], [10,11,12]])\n",
+        "a =np.array([[1,1,0]])\n",
+        "b =np.array([[-1],[2],[5]])\n",
+        "### END CODE HERE\n",
+        "print(\"M = \\n\", M)\n",
+        "print(\"The size of M is: \", M.shape)\n",
+        "print()\n",
+        "print(\"a = \", a)\n",
+        "print(\"The size of a is: \", a.shape)\n",
+        "print()\n",
+        "print(\"b = \", b)\n",
+        "print(\"The size of b is: \", b.shape)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "M = \n",
+            " [[ 1  2  3]\n",
+            " [ 4  5  7]\n",
+            " [ 7  8  9]\n",
+            " [10 11 12]]\n",
+            "The size of M is:  (4, 3)\n",
+            "\n",
+            "a =  [[1 1 0]]\n",
+            "The size of a is:  (1, 3)\n",
+            "\n",
+            "b =  [[-1]\n",
+            " [ 2]\n",
+            " [ 5]]\n",
+            "The size of b is:  (3, 1)\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 = np.dot(a, b)\n",
+        "    ### YOUR CODE HERE\n",
+        "  \n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 3,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "pbLIS5vIE-Ea",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a3383494-bf16-438c-92ba-2fceca4772b0"
+      },
+      "source": [
+        "# 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": 4,
+      "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",
+      "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",
+        "    \n",
+        "    ### YOUR CODE HERE\n",
+        "    \n",
+        "    x=dot_product(a, b).T\n",
+        "    c=a.T\n",
+        "    y=dot_product(M, c).T\n",
+        "\n",
+        "    out = dot_product(x, y).T\n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "metadata": {
+        "id": "M8HmUSLJLYN7"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "da_uQQLhE-Ec",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b4bea7e3-8506-4e70-842b-7f033fca2249"
+      },
+      "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": "71704f96-a95e-48e3-f87f-f5373173ed1d"
+      },
+      "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",
+        "    ### YOUR CODE HERE\n",
+        "    w = None\n",
+        "    v = None\n",
+        "    w,v=np.linalg.eig(M)\n",
+        "    \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",
+        "    ### YOUR CODE HERE\n",
+        "    w,v=eigen_decomp(M)\n",
+        "    eigenvalues =np.ndarray.tolist(w)[0:k]\n",
+        "    eigenvectors =np.ndarray.tolist(v)[0:k]\n",
+        "    \n",
+        "    ### END YOUR CODE\n",
+        "    return eigenvalues, eigenvectors"
+      ],
+      "execution_count": 11,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t0_GkrJwE-Ee",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3f4be8af-b46f-460f-b674-1c1234dffcea"
+      },
+      "source": [
+        "# Let's define M.\n",
+        "M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+        "\n",
+        "# Now let's grab the first eigenvalue and first eigenvector.\n",
+        "# You should get back a single eigenvalue and a single eigenvector.\n",
+        "val, vec = get_eigen_values_and_vectors(M, 1)\n",
+        "print(\"First eigenvalue =\", val[0])\n",
+        "print()\n",
+        "print(\"First eigenvector =\", vec[0])\n",
+        "print()\n",
+        "assert len(vec) == 1\n",
+        "#print (len(vec))\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": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "First eigenvalue = 16.116843969807043\n",
+            "\n",
+            "First eigenvector = [-0.23197068724628617, -0.7858302387420671, 0.40824829046386363]\n",
+            "\n",
+            "Eigenvalues = [16.116843969807043, -1.1168439698070427]\n",
+            "\n",
+            "Eigenvectors = [[-0.23197068724628617, -0.7858302387420671, 0.40824829046386363], [-0.5253220933012336, -0.08675133925662833, -0.816496580927726]]\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(a):\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",
+        "    global i,j\n",
+        "    global c,r\n",
+        "    r=a.shape[0]\n",
+        "    c=a.shape[1]\n",
+        "    p=j\n",
+        "    max=abs(a[j,j])\n",
+        "    for i1 in range (j+1,r):\n",
+        "        flag=abs(a[i1,j])\n",
+        "        if flag>max:\n",
+        "            max=flag  \n",
+        "            p=i1\n",
+        "    swap=0\n",
+        "    for k in range (j,c):\n",
+        "        swap=a[i,k]\n",
+        "        a[i,k]=a[i1,k]\n",
+        "        a[i1,k]=swap\n",
+        "    \n",
+        "    return a\n",
+        "    return out"
+      ],
+      "execution_count": 16,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "S8lbAUSWWpyO"
+      },
+      "source": [
+        "def swap_rows(a):\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",
+        "    global i,j\n",
+        "    global c,r\n",
+        "    r=a.shape[0]\n",
+        "    c=a.shape[1]\n",
+        "    p=j\n",
+        "    max=abs(a[j,j])\n",
+        "    for i1 in range (j+1,r):\n",
+        "        flag=abs(a[i1,j])\n",
+        "        if flag>max:\n",
+        "            max=flag  \n",
+        "            p=i1\n",
+        "    swap=0\n",
+        "    for k in range (j,c):\n",
+        "        swap=a[i,k]\n",
+        "        a[i,k]=a[i1,k]\n",
+        "        a[i1,k]=swap\n",
+        "    \n",
+        "    return a"
+      ],
+      "execution_count": 15,
+      "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",
+        "    ### YOUR CODE HERE\n",
+        "    pass\n",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 23,
+      "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",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 24,
+      "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",
+        "    ### END YOUR CODE\n",
+        "    return out"
+      ],
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Eiz6EbsWPYz8"
+      },
+      "source": [
+        "# Let's define M.\n",
+        "M = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+        "\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)"
+      ],
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "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",
+        "\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 = None\n",
+        "    sum=0\n",
+        "    for i in range (0,len(u)):\n",
+        "      sum=sum + (u[i]-v[i])*(u[i]-v[i])\n",
+        "    pass\n",
+        "    out=sum**0.5\n",
+        "    return out"
+      ],
+      "execution_count": 17,
+      "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",
+        "\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",
+        "    a=u-v\n",
+        "    sum=np.sum(a*a)\n",
+        "    out=np.sqrt(sum)\n",
+        "    \n",
+        "    return out"
+      ],
+      "execution_count": 19,
+      "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": 20,
+      "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": 21,
+      "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": "e992ab7c-c760-4080-e114-77fac10417b5"
+      },
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Native: 1.993574857711792 seconds\n",
+            "NumPy: 0.10695409774780273 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_1/210551_Kushagra_1b(pandas and matplotlib).ipynb b/Assignment/Assignment_1/210551_Kushagra_1b(pandas and matplotlib).ipynb
new file mode 100644
index 0000000..9cb23b5
--- /dev/null
+++ b/Assignment/Assignment_1/210551_Kushagra_1b(pandas and matplotlib).ipynb	
@@ -0,0 +1,1629 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "48645166",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "8a07e324",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.read_csv(\"/Users/kushagragupta/Downloads/House_prediction.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "1d642e41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>city</th>\n",
+       "      <th>area</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>bathroom</th>\n",
+       "      <th>parking spaces</th>\n",
+       "      <th>floor</th>\n",
+       "      <th>animal</th>\n",
+       "      <th>furniture</th>\n",
+       "      <th>hoa (R$)</th>\n",
+       "      <th>rent amount (R$)</th>\n",
+       "      <th>property tax (R$)</th>\n",
+       "      <th>fire insurance (R$)</th>\n",
+       "      <th>total (R$)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>70</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>furnished</td>\n",
+       "      <td>2065</td>\n",
+       "      <td>3300</td>\n",
+       "      <td>211</td>\n",
+       "      <td>42</td>\n",
+       "      <td>5618</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>320</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>not furnished</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>4960</td>\n",
+       "      <td>1750</td>\n",
+       "      <td>63</td>\n",
+       "      <td>7973</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Porto Alegre</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>not furnished</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>2800</td>\n",
+       "      <td>0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>3841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Porto Alegre</td>\n",
+       "      <td>51</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>not furnished</td>\n",
+       "      <td>270</td>\n",
+       "      <td>1112</td>\n",
+       "      <td>22</td>\n",
+       "      <td>17</td>\n",
+       "      <td>1421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>25</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>not acept</td>\n",
+       "      <td>not furnished</td>\n",
+       "      <td>0</td>\n",
+       "      <td>800</td>\n",
+       "      <td>25</td>\n",
+       "      <td>11</td>\n",
+       "      <td>836</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           city  area  rooms  bathroom  parking spaces floor     animal  \\\n",
+       "0     São Paulo    70      2         1               1     7      acept   \n",
+       "1     São Paulo   320      4         4               0    20      acept   \n",
+       "2  Porto Alegre    80      1         1               1     6      acept   \n",
+       "3  Porto Alegre    51      2         1               0     2      acept   \n",
+       "4     São Paulo    25      1         1               0     1  not acept   \n",
+       "\n",
+       "       furniture  hoa (R$)  rent amount (R$)  property tax (R$)  \\\n",
+       "0      furnished      2065              3300                211   \n",
+       "1  not furnished      1200              4960               1750   \n",
+       "2  not furnished      1000              2800                  0   \n",
+       "3  not furnished       270              1112                 22   \n",
+       "4  not furnished         0               800                 25   \n",
+       "\n",
+       "   fire insurance (R$)  total (R$)  \n",
+       "0                   42        5618  \n",
+       "1                   63        7973  \n",
+       "2                   41        3841  \n",
+       "3                   17        1421  \n",
+       "4                   11         836  "
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "28cc0529",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>city</th>\n",
+       "      <th>area</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>bathroom</th>\n",
+       "      <th>parking spaces</th>\n",
+       "      <th>floor</th>\n",
+       "      <th>animal</th>\n",
+       "      <th>furniture</th>\n",
+       "      <th>hoa (R$)</th>\n",
+       "      <th>rent amount (R$)</th>\n",
+       "      <th>property tax (R$)</th>\n",
+       "      <th>fire insurance (R$)</th>\n",
+       "      <th>total (R$)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10687</th>\n",
+       "      <td>Porto Alegre</td>\n",
+       "      <td>63</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>not acept</td>\n",
+       "      <td>furnished</td>\n",
+       "      <td>402</td>\n",
+       "      <td>1478</td>\n",
+       "      <td>24</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10688</th>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>285</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>17</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>not furnished</td>\n",
+       "      <td>3100</td>\n",
+       "      <td>15000</td>\n",
+       "      <td>973</td>\n",
+       "      <td>191</td>\n",
+       "      <td>19260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10689</th>\n",
+       "      <td>Rio de Janeiro</td>\n",
+       "      <td>70</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>not acept</td>\n",
+       "      <td>furnished</td>\n",
+       "      <td>980</td>\n",
+       "      <td>6000</td>\n",
+       "      <td>332</td>\n",
+       "      <td>78</td>\n",
+       "      <td>7390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10690</th>\n",
+       "      <td>Rio de Janeiro</td>\n",
+       "      <td>120</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>furnished</td>\n",
+       "      <td>1585</td>\n",
+       "      <td>12000</td>\n",
+       "      <td>279</td>\n",
+       "      <td>155</td>\n",
+       "      <td>14020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10691</th>\n",
+       "      <td>São Paulo</td>\n",
+       "      <td>80</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-</td>\n",
+       "      <td>acept</td>\n",
+       "      <td>not furnished</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>165</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1587</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 city  area  rooms  bathroom  parking spaces floor     animal  \\\n",
+       "10687    Porto Alegre    63      2         1               1     5  not acept   \n",
+       "10688       São Paulo   285      4         4               4    17      acept   \n",
+       "10689  Rio de Janeiro    70      3         3               0     8  not acept   \n",
+       "10690  Rio de Janeiro   120      2         2               2     8      acept   \n",
+       "10691       São Paulo    80      2         1               0     -      acept   \n",
+       "\n",
+       "           furniture  hoa (R$)  rent amount (R$)  property tax (R$)  \\\n",
+       "10687      furnished       402              1478                 24   \n",
+       "10688  not furnished      3100             15000                973   \n",
+       "10689      furnished       980              6000                332   \n",
+       "10690      furnished      1585             12000                279   \n",
+       "10691  not furnished         0              1400                165   \n",
+       "\n",
+       "       fire insurance (R$)  total (R$)  \n",
+       "10687                   22        1926  \n",
+       "10688                  191       19260  \n",
+       "10689                   78        7390  \n",
+       "10690                  155       14020  \n",
+       "10691                   22        1587  "
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "d78a08aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "d5e507ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10692 entries, 0 to 10691\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column               Non-Null Count  Dtype \n",
+      "---  ------               --------------  ----- \n",
+      " 0   city                 10692 non-null  object\n",
+      " 1   area                 10692 non-null  int64 \n",
+      " 2   rooms                10692 non-null  int64 \n",
+      " 3   bathroom             10692 non-null  int64 \n",
+      " 4   parking spaces       10692 non-null  int64 \n",
+      " 5   floor                10692 non-null  object\n",
+      " 6   animal               10692 non-null  object\n",
+      " 7   furniture            10692 non-null  object\n",
+      " 8   hoa (R$)             10692 non-null  int64 \n",
+      " 9   rent amount (R$)     10692 non-null  int64 \n",
+      " 10  property tax (R$)    10692 non-null  int64 \n",
+      " 11  fire insurance (R$)  10692 non-null  int64 \n",
+      " 12  total (R$)           10692 non-null  int64 \n",
+      "dtypes: int64(9), object(4)\n",
+      "memory usage: 1.1+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "dd3911a5",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>bathroom</th>\n",
+       "      <th>parking spaces</th>\n",
+       "      <th>hoa (R$)</th>\n",
+       "      <th>rent amount (R$)</th>\n",
+       "      <th>property tax (R$)</th>\n",
+       "      <th>fire insurance (R$)</th>\n",
+       "      <th>total (R$)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>1.069200e+04</td>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>10692.000000</td>\n",
+       "      <td>1.069200e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>149.217920</td>\n",
+       "      <td>2.506079</td>\n",
+       "      <td>2.236813</td>\n",
+       "      <td>1.609147</td>\n",
+       "      <td>1.174022e+03</td>\n",
+       "      <td>3896.247194</td>\n",
+       "      <td>366.704358</td>\n",
+       "      <td>53.300879</td>\n",
+       "      <td>5.490487e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>537.016942</td>\n",
+       "      <td>1.171266</td>\n",
+       "      <td>1.407198</td>\n",
+       "      <td>1.589521</td>\n",
+       "      <td>1.559231e+04</td>\n",
+       "      <td>3408.545518</td>\n",
+       "      <td>3107.832321</td>\n",
+       "      <td>47.768031</td>\n",
+       "      <td>1.648473e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>450.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>4.990000e+02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>56.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.700000e+02</td>\n",
+       "      <td>1530.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>2.061750e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>90.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>5.600000e+02</td>\n",
+       "      <td>2661.000000</td>\n",
+       "      <td>125.000000</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>3.581500e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>182.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.237500e+03</td>\n",
+       "      <td>5000.000000</td>\n",
+       "      <td>375.000000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>6.768000e+03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>46335.000000</td>\n",
+       "      <td>13.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>12.000000</td>\n",
+       "      <td>1.117000e+06</td>\n",
+       "      <td>45000.000000</td>\n",
+       "      <td>313700.000000</td>\n",
+       "      <td>677.000000</td>\n",
+       "      <td>1.120000e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               area         rooms      bathroom  parking spaces      hoa (R$)  \\\n",
+       "count  10692.000000  10692.000000  10692.000000    10692.000000  1.069200e+04   \n",
+       "mean     149.217920      2.506079      2.236813        1.609147  1.174022e+03   \n",
+       "std      537.016942      1.171266      1.407198        1.589521  1.559231e+04   \n",
+       "min       11.000000      1.000000      1.000000        0.000000  0.000000e+00   \n",
+       "25%       56.000000      2.000000      1.000000        0.000000  1.700000e+02   \n",
+       "50%       90.000000      2.000000      2.000000        1.000000  5.600000e+02   \n",
+       "75%      182.000000      3.000000      3.000000        2.000000  1.237500e+03   \n",
+       "max    46335.000000     13.000000     10.000000       12.000000  1.117000e+06   \n",
+       "\n",
+       "       rent amount (R$)  property tax (R$)  fire insurance (R$)    total (R$)  \n",
+       "count      10692.000000       10692.000000         10692.000000  1.069200e+04  \n",
+       "mean        3896.247194         366.704358            53.300879  5.490487e+03  \n",
+       "std         3408.545518        3107.832321            47.768031  1.648473e+04  \n",
+       "min          450.000000           0.000000             3.000000  4.990000e+02  \n",
+       "25%         1530.000000          38.000000            21.000000  2.061750e+03  \n",
+       "50%         2661.000000         125.000000            36.000000  3.581500e+03  \n",
+       "75%         5000.000000         375.000000            68.000000  6.768000e+03  \n",
+       "max        45000.000000      313700.000000           677.000000  1.120000e+06  "
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "74ae69a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city1_room=[]\n",
+    "city2_room=[]\n",
+    "city3_room=[]\n",
+    "city4_room=[]\n",
+    "city5_room=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "f7f84495",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for index, row in df.iterrows() :\n",
+    "    if row['city']=='Belo Horizonte':\n",
+    "        city1_room.append(row['rooms'])\n",
+    "    elif row['city']=='Campinas':\n",
+    "        city2_room.append(row['rooms'])\n",
+    "    elif row['city']=='Porto Alegre':\n",
+    "        city3_room.append(row['rooms'])\n",
+    "    elif row['city']=='Rio de Janeiro':\n",
+    "        city4_room.append(row['rooms'])\n",
+    "    elif row['city']=='São Paulo':\n",
+    "        city5_room.append(row['rooms'])\n",
+    "mean_rooms1=sum(city1_room)/len(city1_room)\n",
+    "mean_rooms2=sum(city2_room)/len(city2_room)\n",
+    "mean_rooms3=sum(city3_room)/len(city3_room)\n",
+    "mean_rooms4=sum(city4_room)/len(city4_room)\n",
+    "mean_rooms5=sum(city5_room)/len(city5_room)\n",
+    "list_meanrooms=[mean_rooms1,mean_rooms2,mean_rooms3,mean_rooms4,mean_rooms5]\n",
+    "list_city=['Belo Horizonte','Campinas','Porto Alegre','Rio de Janeiro','São Paulo']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "4771f894",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(list_city,list_meanrooms, color ='maroon',width = 0.4)\n",
+    " \n",
+    "plt.xlabel(\"CITY-->\")\n",
+    "plt.ylabel(\"mean number of rooms-->\")\n",
+    "plt.title(\"plot of mean number of rooms with city\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "645afe18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city1_rent=[]\n",
+    "city2_rent=[]\n",
+    "city3_rent=[]\n",
+    "city4_rent=[]\n",
+    "city5_rent=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "cdd74de0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for index, row in df.iterrows() :\n",
+    "    if row['city']=='Belo Horizonte':\n",
+    "        city1_rent.append(row['rent amount (R$)'])\n",
+    "    elif row['city']=='Campinas':\n",
+    "        city2_rent.append(row['rent amount (R$)'])\n",
+    "    elif row['city']=='Porto Alegre':\n",
+    "        city3_rent.append(row['rent amount (R$)'])\n",
+    "    elif row['city']=='Rio de Janeiro':\n",
+    "        city4_rent.append(row['rent amount (R$)'])\n",
+    "    elif row['city']=='São Paulo':\n",
+    "        city5_rent.append(row['rent amount (R$)'])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "d4d56829",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_rent1=sum(city1_rent)/len(city1_rent)\n",
+    "mean_rent2=sum(city2_rent)/len(city2_rent)\n",
+    "mean_rent3=sum(city3_rent)/len(city3_rent)\n",
+    "mean_rent4=sum(city4_rent)/len(city4_rent)\n",
+    "mean_rent5=sum(city5_rent)/len(city5_rent)\n",
+    "list_meanrent=[mean_rent1,mean_rent2,mean_rent3,mean_rent4,mean_rent5]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "c073ad54",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(list_city,list_meanrent, color ='blue',width = 0.4)\n",
+    " \n",
+    "plt.xlabel(\"CITY-->\")\n",
+    "plt.ylabel(\"mean rent (R$)-->\")\n",
+    "plt.title(\"plot of mean rent with city\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "20b43416",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city1_area=[]\n",
+    "city2_area=[]\n",
+    "city3_area=[]\n",
+    "city4_area=[]\n",
+    "city5_area=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "6d19ca52",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for index, row in df.iterrows() :\n",
+    "    if row['city']=='Belo Horizonte':\n",
+    "        city1_area.append(row['area'])\n",
+    "    elif row['city']=='Campinas':\n",
+    "        city2_area.append(row['area'])\n",
+    "    elif row['city']=='Porto Alegre':\n",
+    "        city3_area.append(row['area'])\n",
+    "    elif row['city']=='Rio de Janeiro':\n",
+    "        city4_area.append(row['area'])\n",
+    "    elif row['city']=='São Paulo':\n",
+    "        city5_area.append(row['area'])\n",
+    "\n",
+    "mean_area1=sum(city1_area)/len(city1_area)\n",
+    "mean_area2=sum(city2_area)/len(city2_area)\n",
+    "mean_area3=sum(city3_area)/len(city3_area)\n",
+    "mean_area4=sum(city4_area)/len(city4_area)\n",
+    "mean_area5=sum(city5_area)/len(city5_area)\n",
+    "list_meanarea=[mean_area1,mean_area2,mean_area3,mean_area4,mean_area5]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "0923509d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(list_city,list_meanarea, color ='red',width = 0.4)\n",
+    " \n",
+    "plt.xlabel(\"CITY-->\")\n",
+    "plt.ylabel(\"mean area-->\")\n",
+    "plt.title(\"plot of mean area with city\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "63821c75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "city1_floor=[]\n",
+    "city2_floor=[]\n",
+    "city3_floor=[]\n",
+    "city4_floor=[]\n",
+    "city5_floor=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "4e1df8c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for index, row in df.iterrows() :\n",
+    "    if row['city']=='Belo Horizonte':\n",
+    "        if row['floor']!='-':\n",
+    "            city1_floor.append(int(row['floor']))\n",
+    "    elif row['city']=='Campinas':\n",
+    "        if row['floor']!='-':\n",
+    "            city2_floor.append(int(row['floor']))\n",
+    "    elif row['city']=='Porto Alegre':\n",
+    "        if row['floor']!='-':\n",
+    "            city3_floor.append(int(row['floor']))\n",
+    "    elif row['city']=='Rio de Janeiro':\n",
+    "        if row['floor']!='-':\n",
+    "            city4_floor.append(int(row['floor']))\n",
+    "    elif row['city']=='São Paulo':\n",
+    "        if row['floor']!='-':\n",
+    "            city5_floor.append(int(row['floor']))\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f580793f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_floor1=sum(city1_floor)/len(city1_floor)\n",
+    "mean_floor2=sum(city2_floor)/len(city2_floor)\n",
+    "mean_floor3=sum(city3_floor)/len(city3_floor)\n",
+    "mean_floor4=sum(city4_floor)/len(city4_floor)\n",
+    "mean_floor5=sum(city5_floor)/len(city5_floor)\n",
+    "list_meanfloor=[mean_floor1,mean_floor2,mean_floor3,mean_floor4,mean_floor5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dcdc5656",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(list_city,list_meanfloor, color ='brown',width = 0.4)\n",
+    " \n",
+    "plt.xlabel(\"CITY-->\")\n",
+    "plt.ylabel(\"mean floor-->\")\n",
+    "plt.title(\"plot of mean floor with city\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ec4294c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '2d Diagram')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'rooms'],df.loc[:,'hoa (R$)'],linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('rooms')\n",
+    "plt.ylabel('hoa')\n",
+    "plt.title('2d Diagram')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "a51ab348",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'this means that hoa does not depend on no of rooms '"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''this means that hoa does not depend on no of rooms '''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "990b455c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '2d Diagram')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'floor'],df.loc[:,'hoa (R$)'],linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('floor')\n",
+    "plt.ylabel('hoa')\n",
+    "plt.title('2d Diagram')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7960fea7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '2d Diagram')"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'area'],df.loc[:,'hoa (R$)'],'r*',linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('rooms')\n",
+    "plt.ylabel('hoa')\n",
+    "plt.title('2d Diagram')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "529d26c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'parking spaces'],df.loc[:,'hoa (R$)'],'r*',linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('parking spaces')\n",
+    "plt.ylabel('hoa')\n",
+    "plt.title('2d Diagram')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c350f6e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>bathroom</th>\n",
+       "      <th>parking spaces</th>\n",
+       "      <th>hoa (R$)</th>\n",
+       "      <th>rent amount (R$)</th>\n",
+       "      <th>property tax (R$)</th>\n",
+       "      <th>fire insurance (R$)</th>\n",
+       "      <th>total (R$)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>furniture</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>furnished</th>\n",
+       "      <td>156.950883</td>\n",
+       "      <td>2.339601</td>\n",
+       "      <td>2.281274</td>\n",
+       "      <td>1.595932</td>\n",
+       "      <td>1267.761704</td>\n",
+       "      <td>4882.28703</td>\n",
+       "      <td>372.097467</td>\n",
+       "      <td>65.229087</td>\n",
+       "      <td>6587.506907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>not furnished</th>\n",
+       "      <td>146.725699</td>\n",
+       "      <td>2.559733</td>\n",
+       "      <td>2.222483</td>\n",
+       "      <td>1.613406</td>\n",
+       "      <td>1143.810660</td>\n",
+       "      <td>3578.46092</td>\n",
+       "      <td>364.966238</td>\n",
+       "      <td>49.456592</td>\n",
+       "      <td>5136.933465</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     area     rooms  bathroom  parking spaces     hoa (R$)  \\\n",
+       "furniture                                                                    \n",
+       "furnished      156.950883  2.339601  2.281274        1.595932  1267.761704   \n",
+       "not furnished  146.725699  2.559733  2.222483        1.613406  1143.810660   \n",
+       "\n",
+       "               rent amount (R$)  property tax (R$)  fire insurance (R$)  \\\n",
+       "furniture                                                                 \n",
+       "furnished            4882.28703         372.097467            65.229087   \n",
+       "not furnished        3578.46092         364.966238            49.456592   \n",
+       "\n",
+       "                total (R$)  \n",
+       "furniture                   \n",
+       "furnished      6587.506907  \n",
+       "not furnished  5136.933465  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_furniture = df.groupby(\"furniture\").agg(np.mean)\n",
+    "df_furniture\n",
+    "                                     "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "31bb7976",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'hoa'}, ylabel='furniture'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "hoa_mean=df_furniture['hoa (R$)']\n",
+    "hoa_mean.plot(kind = \"barh\",  legend = False,\n",
+    "            title = \"hoa\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "119b8465",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '2d Diagram')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'rooms'],df.loc[:,'property tax (R$)'],linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('rooms')\n",
+    "plt.ylabel('property tax')\n",
+    "plt.title('2d Diagram')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "804218d0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '2d Diagram')"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'area'],df.loc[:,'property tax (R$)'])\n",
+    "plt.xlabel('area')\n",
+    "plt.ylabel('property tax')\n",
+    "plt.title('2d Diagram')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "f88ed9be",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>area</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>bathroom</th>\n",
+       "      <th>parking spaces</th>\n",
+       "      <th>hoa (R$)</th>\n",
+       "      <th>rent amount (R$)</th>\n",
+       "      <th>property tax (R$)</th>\n",
+       "      <th>fire insurance (R$)</th>\n",
+       "      <th>total (R$)</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>city</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Belo Horizonte</th>\n",
+       "      <td>207.411765</td>\n",
+       "      <td>3.020668</td>\n",
+       "      <td>2.402226</td>\n",
+       "      <td>1.955485</td>\n",
+       "      <td>2324.197138</td>\n",
+       "      <td>3664.127981</td>\n",
+       "      <td>272.782194</td>\n",
+       "      <td>53.675676</td>\n",
+       "      <td>6315.242448</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Campinas</th>\n",
+       "      <td>137.561547</td>\n",
+       "      <td>2.355217</td>\n",
+       "      <td>1.960141</td>\n",
+       "      <td>1.558030</td>\n",
+       "      <td>628.922626</td>\n",
+       "      <td>2364.290739</td>\n",
+       "      <td>147.657679</td>\n",
+       "      <td>32.388042</td>\n",
+       "      <td>3173.276671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Porto Alegre</th>\n",
+       "      <td>103.609388</td>\n",
+       "      <td>2.140821</td>\n",
+       "      <td>1.725901</td>\n",
+       "      <td>1.044426</td>\n",
+       "      <td>491.618609</td>\n",
+       "      <td>2337.699916</td>\n",
+       "      <td>124.021794</td>\n",
+       "      <td>36.425817</td>\n",
+       "      <td>2989.782900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rio de Janeiro</th>\n",
+       "      <td>105.347768</td>\n",
+       "      <td>2.243837</td>\n",
+       "      <td>1.756163</td>\n",
+       "      <td>0.744171</td>\n",
+       "      <td>1079.432378</td>\n",
+       "      <td>3232.904064</td>\n",
+       "      <td>256.853431</td>\n",
+       "      <td>42.483011</td>\n",
+       "      <td>4611.684877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>São Paulo</th>\n",
+       "      <td>158.899439</td>\n",
+       "      <td>2.558859</td>\n",
+       "      <td>2.467641</td>\n",
+       "      <td>1.877527</td>\n",
+       "      <td>1169.627994</td>\n",
+       "      <td>4652.793783</td>\n",
+       "      <td>495.701716</td>\n",
+       "      <td>62.428911</td>\n",
+       "      <td>6380.831833</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      area     rooms  bathroom  parking spaces     hoa (R$)  \\\n",
+       "city                                                                          \n",
+       "Belo Horizonte  207.411765  3.020668  2.402226        1.955485  2324.197138   \n",
+       "Campinas        137.561547  2.355217  1.960141        1.558030   628.922626   \n",
+       "Porto Alegre    103.609388  2.140821  1.725901        1.044426   491.618609   \n",
+       "Rio de Janeiro  105.347768  2.243837  1.756163        0.744171  1079.432378   \n",
+       "São Paulo       158.899439  2.558859  2.467641        1.877527  1169.627994   \n",
+       "\n",
+       "                rent amount (R$)  property tax (R$)  fire insurance (R$)  \\\n",
+       "city                                                                       \n",
+       "Belo Horizonte       3664.127981         272.782194            53.675676   \n",
+       "Campinas             2364.290739         147.657679            32.388042   \n",
+       "Porto Alegre         2337.699916         124.021794            36.425817   \n",
+       "Rio de Janeiro       3232.904064         256.853431            42.483011   \n",
+       "São Paulo            4652.793783         495.701716            62.428911   \n",
+       "\n",
+       "                 total (R$)  \n",
+       "city                         \n",
+       "Belo Horizonte  6315.242448  \n",
+       "Campinas        3173.276671  \n",
+       "Porto Alegre    2989.782900  \n",
+       "Rio de Janeiro  4611.684877  \n",
+       "São Paulo       6380.831833  "
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_city= df.groupby(\"city\").agg(np.mean)\n",
+    "df_city"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "4ad5b275",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'property tax'}, ylabel='city'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "city_pt_mean=df_city[\"property tax (R$)\"]\n",
+    "city_pt_mean.plot(kind = \"barh\",  legend = False,\n",
+    "            title = \"property tax\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0e8f3bf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'property tax'}, ylabel='furniture'>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "furniture_pt_mean=df_furniture['property tax (R$)']\n",
+    "furniture_pt_mean.plot(kind='barh',legend=False, title='property tax')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "140aa51d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'nearly same property tax for furnished and non furshied house'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''nearly same property tax for furnished and non furshied house'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "1cd058af",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:title={'center':'fire insurance'}, ylabel='furniture'>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "furniture_fi_mean=df_furniture['fire insurance (R$)']\n",
+    "furniture_fi_mean.plot(kind='barh',legend=False, title='fire insurance')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "a5dc3567",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'rooms'],df.loc[:,'fire insurance (R$)'],linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('rooms')\n",
+    "plt.ylabel('fire insurance')\n",
+    "plt.title('2d Diagram')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "79ee2dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'fire insurance increasing with number of rooms'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''fire insurance increasing with number of rooms'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "832b0cf9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'area'],df.loc[:,'fire insurance (R$)'],linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('area')\n",
+    "plt.ylabel('fire insurance')\n",
+    "plt.title('2d Diagram')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "24427ef3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(df.loc[:,'bathroom'],df.loc[:,'fire insurance (R$)'],linestyle='dashed',linewidth=2, markersize=12)\n",
+    "plt.xlabel('bathroom')\n",
+    "plt.ylabel('fire insurance')\n",
+    "plt.title('2d Diagram')\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Assignment/Assignment_2/210551_KUSHAGRA_DL_Stamatics_A2.ipynb b/Assignment/Assignment_2/210551_KUSHAGRA_DL_Stamatics_A2.ipynb
new file mode 100644
index 0000000..e81a776
--- /dev/null
+++ b/Assignment/Assignment_2/210551_KUSHAGRA_DL_Stamatics_A2.ipynb
@@ -0,0 +1,775 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Copy of Copy of DL_Stamatics_A2.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "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",
+      "metadata": {
+        "id": "iXezofBIgzId"
+      },
+      "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)\n",
+        "        self.bias = np.random.random()\n",
+        "\n",
+        "    def activation(self,x):\n",
+        "      return np.heaviside(x,0.5)\n",
+        "    def forward(self,x: np.ndarray):\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",
+        "        y=self.weights\n",
+        "        b=self.bias\n",
+        "        t=x.dot(y)\n",
+        "        v=t+b\n",
+        "        return np.heaviside(v,0.5)\n",
+        "        # YOUR CODE HERE"
+      ],
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oSKwDFAyocVo"
+      },
+      "source": [
+        "np.random.seed(0)\n",
+        "perc = perceptron(8)\n",
+        "assert perc.forward(np.arange(8))==1"
+      ],
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "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 (#classes, #features).\n",
+        "\n",
+        "\n",
+        "\n",
+        "Let's implement this classifier.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9A13CEkGr7uN"
+      },
+      "source": [
+        " import numpy as np\n",
+        "\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)\n",
+        "        self.bias = np.random.rand(num_classes)\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",
+        "        \n",
+        "        # YOUR CODE HERE\n",
+        "        ar=np.dot(x,self.weights)\n",
+        "        return ar+self.bias\n",
+        "        # YOUR CODE HERE"
+      ],
+      "execution_count": 14,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zgzPxyTsr7uN",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e0a14a54-17ea-4d5a-a6ad-4d706d37d13a"
+      },
+      "source": [
+        "np.random.seed(0)\n",
+        "lc = LinearClassifier()\n",
+        "lc.forward(np.random.rand(1,32))\n",
+        "# Should be close to:\n",
+        "# array([[ 1.30208164,  5.58136003,  0.87793013, -4.7332119 ,  4.81172123]])"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 1.30208164,  5.58136003,  0.87793013, -4.7332119 ,  4.81172123]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 19
+        }
+      ]
+    },
+    {
+      "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",
+      "metadata": {
+        "id": "YxVh6IL2tuqz"
+      },
+      "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)*abs(y-z)\n",
+        "    return L"
+      ],
+      "execution_count": 20,
+      "outputs": []
+    },
+    {
+      "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",
+      "metadata": {
+        "id": "JThp5P-KcKtS"
+      },
+      "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)*abs(y-z)*abs(y-z)\n",
+        "    pass"
+      ],
+      "execution_count": 21,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z2JNLnWYcLSC"
+      },
+      "source": [
+        "### **Hinge Loss**\n",
+        "Hinge loss is: $ L = max( 0, 1 - yz ) $"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gQ1YM4J-cLSC"
+      },
+      "source": [
+        "import numpy as np\n",
+        "def hingeLoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "  \n",
+        "    return max(0,1-y*z)"
+      ],
+      "execution_count": 25,
+      "outputs": []
+    },
+    {
+      "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",
+      "metadata": {
+        "id": "snJLqhszdMNY"
+      },
+      "source": [
+        "import numpy as np\n",
+        "import math\n",
+        "def CELoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    return -(y*math.log(z) + (1-y)*math.log((1-z)))"
+      ],
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "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",
+      "metadata": {
+        "id": "5sA7GxLHyEVM"
+      },
+      "source": [
+        "import numpy as np\n",
+        "def zeroOneLoss(z,y):\n",
+        "    '''\n",
+        "        y : True output. \n",
+        "        z : Predicted output. \n",
+        "        return : L\n",
+        "    '''\n",
+        "    if(np.array_equal(z,y)):\n",
+        "        return 1\n",
+        "    else:\n",
+        "        return 0"
+      ],
+      "execution_count": 27,
+      "outputs": []
+    },
+    {
+      "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",
+        "$$\\boxed{J(\\theta)=\\frac{1}{m}\\sum_{i=1}^mL(h_\\theta(x^{(i)}), y^{(i)})}$$\n",
+        "where $h_\\theta$ is the hypothesis function i.e. the function used to predict the output."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SSbmhW4og97t"
+      },
+      "source": [
+        "llossFunctions = {\n",
+        "    \"l1\" : L1Loss,\n",
+        "    \"l2\" : L2Loss,\n",
+        "    \"hinge\" : hingeLoss,\n",
+        "    \"cross-entropy\" : CELoss,\n",
+        "    \"0-1\" : zeroOneLoss\n",
+        "}\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 = np.sum(loss_func(Z , Y))\n",
+        "\n",
+        "    # YOUR CODE HERE\n",
+        "    "
+      ],
+      "execution_count": 40,
+      "outputs": []
+    },
+    {
+      "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",
+      "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."
+      ],
+      "metadata": {
+        "id": "sJoG5kkYopRN"
+      }
+    },
+    {
+      "cell_type": "code",
+      "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"
+      ],
+      "metadata": {
+        "id": "_4-4RceVsor_",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d0b68d88-d405-41a9-bf79-97b749590aa3"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "2.8.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yyplk5PLEUsJ",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "01a4f462-b1fd-4bd5-ee6a-04dd7463d3ce"
+      },
+      "source": [
+        "# Load in the data\n",
+        "cifar10 = tf.keras.datasets.cifar10\n",
+        " \n",
+        "# Distribute it to train and test set\n",
+        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\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()"
+      ],
+      "execution_count": 42,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
+            "170500096/170498071 [==============================] - 11s 0us/step\n",
+            "170508288/170498071 [==============================] - 11s 0us/step\n",
+            "(50000, 32, 32, 3) (50000, 1) (10000, 32, 32, 3) (10000, 1)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qQhkATYhEkkC",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 268
+        },
+        "outputId": "d5700b09-7a86-42cf-a228-05afaaa0d478"
+      },
+      "source": [
+        "'''visualize data by plotting images'''\n",
+        "# YOUR CODE HERE\n",
+        "fig, a = plt.subplots(3, 3)\n",
+        "z= 0\n",
+        "\n",
+        "for i in range(3):\n",
+        "    for j in range(3):\n",
+        "        a[i][j].imshow(x_train[z], aspect='auto')\n",
+        "        z=z+1\n",
+        "\n",
+        "plt.show()\n",
+        "# YOUR CODE HERE"
+      ],
+      "execution_count": 58,
+      "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"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yJgho2AEBFbx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5fb08667-2f5a-4f5d-b457-6dd7556e950f"
+      },
+      "source": [
+        "# number of classes\n",
+        "K = len(set(y_train))\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",
+        "model=tf.keras.models.Sequential()\n",
+        "model.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding=\"same\", activation=\"relu\", input_shape=[32,32,3]))\n",
+        "model.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2,padding='valid'))\n",
+        "model.add(tf.keras.layers.Flatten())\n",
+        "model.add(tf.keras.layers.Dropout(0.5,noise_shape=None,seed=None))\n",
+        " \n",
+        "'''Hidden layer'''\n",
+        "# YOUR CODE HERE\n",
+        "pass\n",
+        "model.add(tf.keras.layers.Dense(units=128,activation='relu'))\n",
+        "# YOUR CODE HERE\n",
+        " \n",
+        "\"\"\"last hidden layer i.e.. output layer\"\"\"\n",
+        "# YOUR CODE HERE\n",
+        "pass\n",
+        "model.add(tf.keras.layers.Dense(units=10,activation='softmax'))\n",
+        "# YOUR CODE HERE\n",
+        "model.summary()\n",
+        " \n"
+      ],
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "number of classes: 10\n",
+            "Model: \"sequential_1\"\n",
+            "_________________________________________________________________\n",
+            " Layer (type)                Output Shape              Param #   \n",
+            "=================================================================\n",
+            " conv2d_1 (Conv2D)           (None, 32, 32, 32)        896       \n",
+            "                                                                 \n",
+            " max_pooling2d_1 (MaxPooling  (None, 16, 16, 32)       0         \n",
+            " 2D)                                                             \n",
+            "                                                                 \n",
+            " flatten_1 (Flatten)         (None, 8192)              0         \n",
+            "                                                                 \n",
+            " dropout_1 (Dropout)         (None, 8192)              0         \n",
+            "                                                                 \n",
+            " dense_2 (Dense)             (None, 128)               1048704   \n",
+            "                                                                 \n",
+            " dense_3 (Dense)             (None, 10)                1290      \n",
+            "                                                                 \n",
+            "=================================================================\n",
+            "Total params: 1,050,890\n",
+            "Trainable params: 1,050,890\n",
+            "Non-trainable params: 0\n",
+            "_________________________________________________________________\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PLc4Bay65TyA"
+      },
+      "source": [
+        "# Compile\n",
+        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"Adam\")\n"
+      ],
+      "execution_count": 71,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Fit\n",
+        "history = model.fit(x_train, y_train, epochs=2)\n"
+      ],
+      "metadata": {
+        "id": "U0fGsDCRsQrn",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9fe99904-2603-49d6-8d71-9451c892d934"
+      },
+      "execution_count": 74,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/2\n",
+            "1563/1563 [==============================] - 52s 33ms/step - loss: 1.2930\n",
+            "Epoch 2/2\n",
+            "1563/1563 [==============================] - 52s 33ms/step - loss: 1.1764\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# label mapping\n",
+        " \n",
+        "# label mapping\n",
+        " \n",
+        "labels = '''airplane automobile bird cat deerdog frog horseship truck'''.split()\n",
+        " \n",
+        "# select the image from our test dataset\n",
+        "image_number = 17\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))"
+      ],
+      "metadata": {
+        "id": "RDq_RE6osSh8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 284
+        },
+        "outputId": "652f674f-55d0-4a19-d291-f473197c84e3"
+      },
+      "execution_count": 76,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Original label is truck and predicted label is truck\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"
+          }
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file