diff --git a/AishikRakshit_190122002_W03.ipynb b/AishikRakshit_190122002_W03.ipynb
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@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "# The first two columns contains the exam scores and the third column\n",
+    "# contains the label.\n",
+    "data = np.loadtxt(r'C:\\Users\\LENOVO\\Desktop\\Machine learning\\machine-learning-ex\\ex2\\ex2data1.txt', delimiter=',')\n",
+    "X, y = data[:, 0:2], data[:, 2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(X, y):\n",
+    "    \"\"\"\n",
+    "    Plots the data points X and y into a new figure. Plots the data \n",
+    "    points with * for the positive examples and o for the negative examples.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        An Mx2 matrix representing the dataset. \n",
+    "    \n",
+    "    y : array_like\n",
+    "        Label values for the dataset. A vector of size (M, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Plot the positive and negative examples on a 2D plot, using the\n",
+    "    option 'k*' for the positive examples and 'ko' for the negative examples.    \n",
+    "    \"\"\"\n",
+    "    # Create New Figure\n",
+    "    fig = pyplot.figure()\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    pos = y == 1\n",
+    "    neg = y == 0\n",
+    "    pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "    pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
+    "    \n",
+    "    \n",
+    "    # ============================================================"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": [
+    "plotData(X, y)\n",
+    "# add axes labels\n",
+    "pyplot.xlabel('Exam 1 score')\n",
+    "pyplot.ylabel('Exam 2 score')\n",
+    "pyplot.legend(['Admitted', 'Not admitted'])\n",
+    "pass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(z):\n",
+    "    \"\"\"\n",
+    "    Compute sigmoid function given the input z.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    z : array_like\n",
+    "        The input to the sigmoid function. This can be a 1-D vector \n",
+    "        or a 2-D matrix. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    g : array_like\n",
+    "        The computed sigmoid function. g has the same shape as z, since\n",
+    "        the sigmoid is computed element-wise on z.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n",
+    "    \"\"\"\n",
+    "    # convert input to a numpy array\n",
+    "    z = np.array(z)\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    g = np.zeros(z.shape)\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    g=1/(1+np.exp(-z))\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g( 0 ) =  0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test the implementation of sigmoid function here\n",
+    "z = 0\n",
+    "g = sigmoid(z)\n",
+    "\n",
+    "print('g(', z, ') = ', g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+    "m, n = X.shape\n",
+    "\n",
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunction(theta, X, y):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        The parameters for logistic regression. This a vector\n",
+    "        of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1) where m is the total number\n",
+    "        of data points and n is the number of features. We assume the \n",
+    "        intercept has already been added to the input.\n",
+    "    \n",
+    "    y : arra_like\n",
+    "        Labels for the input. This is a vector of shape (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n+1, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to \n",
+    "    the cost. Compute the partial derivatives and set grad to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    z=np.dot(X,theta)\n",
+    "    A=sigmoid(z)\n",
+    "    J =-1/m * np.sum( np.multiply(np.log(A), y) + np.multiply(np.log(1-A), (1-y)))\n",
+    "    #J=-(1/m)*sum(y*np.log(g)+(1-y)*(1-np.log(g)))\n",
+    "    grad=(1/m)*X.transpose()@(g-y)\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost\n",
+      "0.6931471805599453\n",
+      "Gradient at initial theta (zeros):\n",
+      "[ -0.1        -12.00921659 -11.26284221]\n",
+      "Cost\n",
+      "0.21833019382659774\n",
+      "Gradient at initial theta (zeros):\n",
+      "[ -0.1        -12.00921659 -11.26284221]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(n+1)\n",
+    "\n",
+    "cost, grad = costFunction(initial_theta, X, y)\n",
+    "\n",
+    "print(\"Cost\")\n",
+    "print(cost)\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print(grad)\n",
+    "\n",
+    "test_theta = np.array([-24, 0.2, 0.2])\n",
+    "cost, grad = costFunction(test_theta, X, y)\n",
+    "\n",
+    "print(\"Cost\")\n",
+    "print(cost)\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print(grad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6394269623737789\n",
+      "theta:\n",
+      "[6.39239128e-05 7.67676114e-03 7.19964943e-03]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 400}\n",
+    "\n",
+    "# see documention for scipy's optimize.minimize  for description about\n",
+    "# the different parameters\n",
+    "# The function returns an object `OptimizeResult`\n",
+    "# We use truncated Newton algorithm for optimization which is \n",
+    "# equivalent to MATLAB's fminunc\n",
+    "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_theta,\n",
+    "                        (X, y),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "# the fun property of `OptimizeResult` object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property\n",
+    "theta = res.x\n",
+    "print(cost)\n",
+    "print('theta:')\n",
+    "print(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(theta, X):\n",
+    "    \"\"\"\n",
+    "    Predict whether the label is 0 or 1 using learned logistic regression.\n",
+    "    Computes the predictions for X using a threshold at 0.5 \n",
+    "    (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Parameters for logistic regression. A vecotor of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data to use for computing predictions. The rows is the number \n",
+    "        of points to compute predictions, and columns is the number of\n",
+    "        features.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    p : array_like\n",
+    "        Predictions and 0 or 1 for each row in X. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned \n",
+    "    logistic regression parameters.You should set p to a vector of 0's and 1's    \n",
+    "    \"\"\"\n",
+    "    m = X.shape[0]\n",
+    "    # Number of training examples\n",
+    "    m\n",
+    "    # You need to return the following variables correctly\n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    z=X*theta\n",
+    "    h=sigmoid(z)\n",
+    "    p=np.round(p)\n",
+    "        \n",
+    "            \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40.0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+    "pr = predict(theta, X)\n",
+    "np.mean(pr == y) * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Data\n",
+    "# The first two columns contains the X values and the third column\n",
+    "# contains the label (y).\n",
+    "data = np.loadtxt(r'C:\\Users\\LENOVO\\Desktop\\Machine learning\\machine-learning-ex\\ex2\\ex2data2.txt', delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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": [
+    "plotData(X, y)\n",
+    "# Labels and Legend\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "\n",
+    "# Specified in plot order\n",
+    "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunctionReg(theta, X, y, Lambda):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression with regularization.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Logistic regression parameters. A vector with shape (n, ). n is \n",
+    "        the number of features including any intercept. If we have mapped\n",
+    "        our initial features into polynomial features, then n is the total \n",
+    "        number of polynomial features. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data set with shape (m x n). m is the number of examples, and\n",
+    "        n is the number of features (after feature mapping).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector with shape (m, ).\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The regularization parameter. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the regularized cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost `J` of a particular choice of theta.\n",
+    "    Compute the partial derivatives and set `grad` to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ===================== YOUR CODE HERE ======================\n",
+    "    theta2=np.concatenate(0,theta[1:n+1])\n",
+    "    z=np.dot(X,theta)\n",
+    "    A=sigmoid(z)\n",
+    "    J=-1/m * np.sum( np.multiply(np.log(A), y) + np.multiply(np.log(1-A), (1-y)))+(Lambda /(2*m))*sum(theta[1:n+1]^2)\n",
+    "    grad=(1/m)*X.transpose()*(g-y)+(Lambda/m)*sum(theta2[1:n+1])\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/README.md b/Phase 3 - 2020 (Summer)/README.md
index 9818b1173..87a6eae8e 100644
--- a/Phase 3 - 2020 (Summer)/README.md	
+++ b/Phase 3 - 2020 (Summer)/README.md	
@@ -1,3 +1,9 @@
+Aishik Rakshit
+190122002
+CST
+https://github.com/Aishik13012002
+
+
 # IITG.ai
 
 - Resources include course/blog/tutorial/research paper links as well as details regarding graded assignments as well as prospective project ideas.
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/Graphs_w01.png b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/Graphs_w01.png
new file mode 100644
index 000000000..dfc56e078
Binary files /dev/null and b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/Graphs_w01.png differ
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/Result_w01.txt b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/Result_w01.txt
new file mode 100644
index 000000000..792c9ca6c
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/Result_w01.txt	
@@ -0,0 +1,5 @@
+RESULT
+
+
+
+THE two features that describe the two labels perfectly are feature 3 and 8
\ No newline at end of file
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/data.csv b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/data.csv
new file mode 100644
index 000000000..67bec0c73
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/data.csv	
@@ -0,0 +1,1000 @@
+Label	1	2	3	4	5	6	7	8	9	10
+2	3.201	5.774701479	3.429271509	0.108731597	-0.146594209	-2.140013156	8.025347331	-0.082736883	-0.316597925	8.970594702
+1	1.066	0.519693458	5.936319736	0.19472858	-1.031532563	0.678118588	12.40887466	-0.057266412	0.129536154	6.406818035
+2	1.395	6.18460515	4.393926764	0.236127673	-0.306629726	-1.152583351	12.27118782	-0.216143346	0.654901697	17.42562827
+1	0.062	0.106773671	0.227877344	0.241524643	-1.734882845	-2.111483361	12.53193079	0.991367763	0.012839022	3.487370091
+2	1.416	7.687775933	3.696550805	0.416806978	-0.16730342	-1.977925232	12.91515299	-0.142540374	0.773265509	20.23465854
+2	3.929	5.109210021	1.878944788	0.502120872	0.657464241	-0.527106429	9.179846913	0.507144633	-0.359042395	16.46087899
+2	3.947	5.859324469	1.153662192	0.507744628	0.533879437	0.229925275	8.75453989	0.79247962	-0.274797071	9.065836314
+2	2.529	5.775247364	1.215303078	0.511342921	-0.493071807	-0.073267454	12.74701658	0.77139188	0.51039382	8.659029406
+2	0.414	6.431543842	2.74162632	0.658268566	-0.287392126	-2.756843379	10.78617133	0.142027865	-0.491328216	10.90229338
+2	5.145	2.261374009	1.862050936	0.722430263	-0.789449606	1.2384776	8.796320955	0.514424361	-1.518901366	19.04618511
+1	0.848	1.190165636	0.009058933	0.794686041	-2.145244465	-3.338397625	8.153719346	0.999986323	-1.134761919	0.074790793
+2	1.614	7.670741144	4.080048519	0.833665094	-1.628040868	-0.50904631	10.19672228	-0.197705106	-1.714917781	6.65390479
+1	0.217	0.396703105	4.809722968	0.837892989	0.568168004	1.307683566	10.4133593	-0.20692809	-0.011786561	4.305879625
+2	3.317	3.308198661	0.779481424	0.845310715	-2.614122286	-1.727452636	13.0072622	0.901767047	0.4010224	3.717246878
+2	3.131	5.821287306	3.226665913	0.881875306	-0.037554626	0.486201841	9.110740249	-0.026333894	-0.98863987	12.66371236
+2	1.344	4.293829549	2.809000749	0.886208818	-2.350962834	-1.565452513	8.708318348	0.116231364	-1.464931916	1.989717961
+2	0.152	6.864823033	2.811000366	0.895119349	-1.589874574	1.281363099	8.927791344	0.115476079	-1.878841284	9.289314166
+2	1.708	5.721147734	4.484196331	0.90672248	-1.010086915	-1.999191504	9.989481679	-0.217224396	-1.691625265	3.391231956
+2	5.456	5.083440795	4.519788746	0.927002184	-1.48451198	0.903325087	10.72504975	-0.217158344	-1.263516749	9.993585963
+1	0.069	1.181511824	5.59886212	0.933222048	0.662457752	-3.234787128	9.507136583	-0.112906644	-0.381553842	0.009255953
+1	0.239	0.372213585	3.985110838	0.937386792	0.246621381	-2.854679277	10.46659516	-0.187444414	-0.260523294	4.056078289
+2	0.805	4.933173454	1.167043118	0.959987224	-1.835346251	0.393483345	12.2189491	0.787968266	-0.024956535	8.259165241
+2	4.546	1.745217799	4.868811436	1.022118418	0.513577926	-1.161132237	10.01918093	-0.202881328	-0.337186264	15.29394489
+1	0.287	0.504174541	5.695678041	1.049916708	-0.074419017	0.214672224	13.66762054	-0.097317295	0.378131482	2.798778083
+1	1.038	0.454658455	1.226700725	1.082608629	-2.269898568	-0.138134034	10.25195093	0.767408801	-1.442379448	0.497807316
+2	5.911	3.826092573	3.771601701	1.193634215	0.217775796	-1.43568543	9.484560203	-0.156207393	-0.782155084	16.62931827
+1	1.09	2.334084775	4.557174885	1.263455257	-1.253495063	-0.773167385	10.08923826	-0.216796257	-1.737330591	0.588804781
+1	0.256	0.166154069	5.056914487	1.26665146	0.53178581	1.578253148	13.52595532	-0.186128482	1.080933487	0.004179179
+1	1.628	2.576641092	2.591818329	1.285181423	0.663130582	-1.98203899	13.24315666	0.201593922	1.395176587	0.030773643
+2	1.467	5.632987643	3.626435223	1.293531487	-0.184272841	-2.601193964	11.71793912	-0.128519896	0.477928987	5.809889103
+1	0.655	0.700822806	2.170700721	1.337013126	0.15818828	1.62701567	9.529426791	0.380241083	-0.83699994	1.600552609
+1	0.689	1.24906167	1.47822918	1.340410061	-1.804109441	-1.974668159	8.912532606	0.673588868	-1.844551915	0.722083214
+1	0.282	0.530288935	2.284123292	1.406141891	-2.883779716	-0.331620151	13.92980084	0.331063151	-0.049083237	4.049282386
+1	0.033	0.219511263	4.187918729	1.440052638	-1.310997395	-0.786621947	11.65971929	-0.206687234	-0.350055554	0.735248847
+1	2.855	1.513249056	4.349711097	1.450819401	-1.649233148	-1.772435315	9.519615275	-0.214945338	-1.992431721	0.133112985
+1	2.325	2.615973781	5.460528127	1.45312046	0.008044936	-0.6480863	13.31045752	-0.134228048	0.743751498	0.127225423
+2	2.376	6.987846865	2.834295762	1.458592683	0.438076347	-1.3443138	9.174020839	0.106722547	-0.54452658	14.39299066
+1	2.334	2.028196599	3.57235516	1.469459307	0.691349961	1.265475298	13.29548261	-0.116887531	1.383344043	5.891642746
+2	2.567	4.707382344	1.316293273	1.484608366	-1.718696931	-1.125464905	8.109229913	0.735237768	-1.24156828	16.62554193
+1	0.687	0.669629569	0.794321228	1.50303362	0.308057145	0.642458073	8.477762062	0.898110338	-0.280899994	6.485718074
+1	0.065	1.817468901	2.965328325	1.526947678	-0.707022399	-0.341237239	13.57656164	0.059134435	-0.117873837	4.188634989
+2	1.37	4.427705724	1.107976019	1.533912529	-1.417341341	0.414383607	9.302231873	0.807595877	-1.980749491	22.64865363
+1	0.435	2.767820777	0.605948128	1.536012759	-2.584916232	-3.10300895	11.14989728	0.939918174	-0.374656182	2.130332115
+1	1.141	1.63127238	2.97184208	1.572157304	0.586475087	-1.191131161	9.353649472	0.056845723	-0.444042857	5.314512291
+2	6.81	1.759671045	5.720527553	1.572943074	0.49537979	-3.06190978	12.78817083	-0.093249462	1.450868827	17.54195318
+1	1.352	1.711880752	3.768287102	1.637122384	-1.553361329	-2.386130457	12.4133259	-0.155633197	-0.011536515	1.790607417
+1	0.043	1.194686395	3.607295953	1.647313583	-0.999318117	-0.482077696	13.09668368	-0.12448419	0.021546396	0.087105491
+2	5.09	3.968031526	5.734444699	1.669332872	0.390408185	-0.25010689	10.78786793	-0.090961406	0.174349814	18.20819374
+1	1.488	1.912472403	1.278502297	1.671831384	-0.987584434	1.495747985	12.15695115	0.74898995	0.082653931	2.143953118
+1	0.61	0.553222635	4.566035705	1.676660945	-2.308869887	-1.362853943	11.94493763	-0.216667045	0.073278846	3.657229264
+2	1.787	5.561928631	5.911165939	1.684915079	-2.943675877	-1.844207595	13.60180785	-0.061493352	0.313522721	28.11561756
+1	0.801	1.069876543	4.273459535	1.69449472	-1.141973161	1.274411084	9.452997825	-0.211820687	-1.909057554	0.161329861
+1	2.801	2.757962272	4.339160845	1.702482753	-0.273205064	-1.319645021	10.31642716	-0.214593369	-0.897948631	0.633947702
+2	0.853	4.270324139	5.268276438	1.704339408	-2.181409199	-1.72127642	11.85761355	-0.161235363	-0.060125291	8.192364685
+1	0.004	0.008236368	5.348466688	1.713798348	-0.082330058	-2.052878186	13.68365881	-0.150404219	0.355884654	0.013069244
+1	1.817	0.176709431	0.096234332	1.717203343	-0.89227169	-0.083190909	10.25985539	0.998457207	-1.449619849	0.296442053
+2	1.781	5.437113183	0.394153888	1.719117312	-2.679714471	-0.976574322	13.66365007	0.974307509	0.010388734	0
+1	0.147	0.035490624	0.975969116	1.737152833	-2.240083083	-2.684534055	12.58616295	0.848638879	0.215539757	0.044655805
+1	0.537	1.515310246	2.654600351	1.744852664	-1.080130187	-2.182970254	13.49329978	0.176286413	-0.281726362	4.315446926
+1	0.599	2.32112157	3.151968761	1.745301605	-1.175646804	0.480829271	12.52231321	-0.003291886	0.076090613	5.92549503
+1	1.675	1.325416235	2.20132442	1.752870801	-0.55770048	-0.931094008	8.541686412	0.366922869	-1.164001841	0.045149609
+1	0.314	3.149940303	4.792381288	1.767728358	-0.877718571	-0.089517148	8.106868645	-0.20799729	-1.019483444	0.90741143
+2	0.711	5.228195328	2.095859617	1.773089714	-0.62392161	-1.238236174	11.23879694	0.412857908	-0.343390731	21.70573876
+2	3.064	3.477502438	2.504833738	1.775871849	0.148323676	0.73406276	8.712798188	0.23737809	-0.609289485	7.666223198
+1	0.589	1.757028892	3.062811868	1.784991404	-2.115913409	-2.81330715	12.37064993	0.02569512	0.125841183	5.201048456
+1	3.008	1.876829636	4.624403672	1.800243643	-1.785302737	-0.962108336	11.90752744	-0.21540762	-0.18638061	0.038193836
+2	2.838	6.262713718	3.378285329	1.842836398	-1.340719655	-2.14609091	11.57803475	-0.069410597	-0.423568555	3.040522146
+1	1.043	1.596998572	4.56959105	1.851251429	-2.722948041	-1.717233632	11.54080787	-0.216610563	0.112095327	1.00926046
+1	0.772	0.549146131	1.840798477	1.860470382	-1.468595686	-0.750676145	13.57214128	0.523561017	-0.459344568	0.823038401
+2	0.073	7.452051295	2.030806709	1.873999843	-0.158166761	-3.430548458	9.072096059	0.44122756	-1.095957834	14.50005087
+1	0.496	0.293423878	5.700223227	1.883652986	-1.92902841	-3.034345137	10.49703962	-0.096575025	-1.414657231	1.506036851
+2	5.985	3.199868081	1.772757495	1.884835748	-0.55731878	-0.062191763	10.87164483	0.552627797	-0.652525085	29.14240637
+1	1.307	1.35615201	0.206224491	1.890188115	-0.397572583	-2.073332024	11.73424352	0.992926967	0.286123186	0.070237834
+1	2.276	2.090608128	0.613697117	1.890341294	-1.229778304	-0.264521158	9.724885185	0.938400812	-1.897719476	3.434150374
+1	0.672	1.041359327	1.2059452	1.901769053	-0.812723015	0.535504583	13.03158366	0.774642903	0.167564009	0.874543595
+1	0.324	0.977855156	1.714963901	1.904489409	-0.431800016	0.143927508	8.290451544	0.577053454	-0.841249243	0.489578643
+1	1.484	2.496255175	4.752636969	1.904497396	-1.43315907	0.019658426	13.23137098	-0.210239108	-0.203626379	0.418088518
+1	0.026	2.876568958	5.72315451	1.907911002	-1.701573349	-2.921623623	9.560252151	-0.092818094	-1.982298264	2.342196248
+2	2.562	3.441728791	1.565619397	1.937040287	-1.880895244	-3.124273792	10.6632892	0.638716282	-1.278507361	20.03046752
+1	0.727	1.248158776	3.956537158	1.965104121	0.260404852	-0.80732286	10.6755922	-0.183920308	-0.057077778	6.845602121
+1	0.54	1.526414698	4.5144264	2.00015614	-1.222890564	-3.205307286	8.75766466	-0.217185802	-1.725699759	0.167622454
+2	7.499	1.530997493	2.093647513	2.016126465	-0.959043358	-1.05300359	11.79401459	0.413822743	-0.10237397	0.205300615
+1	2.118	2.427081198	0.773881531	2.0235226	-0.599017994	-1.643445368	10.56237592	0.903131231	-0.983607592	6.999109663
+2	1.117	6.24716603	5.949579377	2.04482442	-0.176303535	0.446165119	11.9171089	-0.055037887	0.621138768	8.355192805
+2	5.269	3.801569088	0.378862884	2.068547524	-2.276255308	-2.744220384	11.9746276	0.976248258	0.068655633	18.4482071
+2	3.768	4.471176408	1.871194557	2.113885246	0.438186107	0.584544181	13.42318685	0.510486056	1.079144594	11.95093832
+2	4.7	5.352186929	4.191928745	2.156088223	-1.442048771	1.516858208	11.24662261	-0.206966878	-0.743303919	28.04092769
+1	1.038	1.354605421	4.109062235	2.170151874	-0.046242506	-2.415842316	9.8928148	-0.200399162	-0.938681685	0.947735937
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+2	3.468	2.402792775	2.114706965	11.19458199	-0.373581779	-1.724797349	13.52575944	0.404638346	0.209068055	30.95011076
+1	1.37	0.439298355	3.045842432	11.20903752	-0.573306223	-2.135616352	13.46481933	0.031388354	0.080411779	0.695285824
+1	2.417	2.922905188	1.807295892	11.21498577	-2.03656849	-2.424590391	8.101951857	0.537910859	-1.138911819	1.201552536
+1	2.81	1.354140547	5.366312814	11.28944355	-2.734259401	0.362152459	8.764868766	-0.147904726	-1.186210086	0.377183867
+2	1.519	4.380450816	2.49792002	11.36026624	-1.958327857	0.76884129	11.89750479	0.240254775	-0.141319258	3.346551639
+2	5.458	4.471458128	4.959344077	11.39540982	-0.401164209	-2.353390711	10.76781568	-0.195522076	-0.61628497	17.64311372
+2	2.113	7.658276558	1.920879894	11.40975617	-2.868869469	-0.366854543	8.218143628	0.489017589	-0.625521339	5.593306053
+2	1.377	4.844674591	2.190547572	11.44174929	-2.55957236	-1.870973509	12.61313971	0.371607071	0.449193808	22.22504205
+1	2.632	2.616838109	0.084600044	11.49757994	-1.839119028	-1.543901078	9.341708797	0.998807566	-1.960768669	0.414875464
+2	1.482	4.727302582	3.862833756	11.59975458	-2.00842928	1.405114156	13.3566487	-0.170941147	-0.202109598	3.911158906
+1	1.866	2.950051157	1.702322057	11.65534373	-1.059964037	-0.484756103	13.56664011	0.582359224	-0.332262389	0.016424186
+1	0.966	2.8035431	4.665995584	11.68365289	-1.941228539	-2.86043659	9.749072625	-0.214085933	-1.880046675	0.004106537
+1	0.854	0.977449368	5.408349873	11.69025452	0.066928772	-2.310024945	11.28825567	-0.141898735	0.355399432	0.060236838
+1	0.03	0.093711788	3.678407315	11.87139315	-0.085894958	-0.717799527	13.5153185	-0.139027914	0.496749195	0.079339821
+2	3.881	5.772867182	0.97386923	11.91699859	-0.62227508	-1.354237917	10.65049178	0.849258588	-0.921159618	17.13040574
diff --git a/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/iitg_ml....w01.py b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/iitg_ml....w01.py
new file mode 100644
index 000000000..2c4631f31
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 1 (Mar 28 - Apr 4)/assignment/AISHIK RAKSHIT_190122002/iitg_ml....w01.py	
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Apr  3 14:54:41 2020
+
+@author: LENOVO
+"""
+
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Apr  2 19:13:26 2020
+
+@author: LENOVO
+"""
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+plt.style.use('ggplot')
+
+
+ds = pd.read_csv(r"C:\Users\LENOVO\Desktop\data.csv", delimiter='\t')
+ds1=ds.loc[ds['Label']==1]
+ds2=ds.loc[ds['Label']==2]
+
+
+plt.rcParams['figure.figsize']=(30,30)
+fig, axes=plt.subplots(10,10)
+
+
+
+
+for i in range(1,11):
+    for j in range(1,11):
+        if(i!=j):
+            x=str(i)
+            y=str(j)
+            ds1=ds.loc[ds['Label']==1].sort_values(by=x)
+            ds2=ds.loc[ds['Label']==2].sort_values(by=x)
+            x_c1=ds1[[x]]
+            y_c1=ds1[[y]]
+            x_c2=ds2[[x]]
+            y_c2=ds2[[y]]
+            axes[i-1][j-1].plot(x_c1,y_c1,'r',label='1')
+            axes[i-1][j-1].plot(x_c2,y_c2,'b',label='2') 
+            axes[i-1][j-1].set_xlabel(x)
+            axes[i-1][j-1].set_ylabel(y)
+
+        
+fig.tight_layout(pad=2.0)
+plt.show()
\ No newline at end of file
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/AishikRakshit_190122002.ipynb b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/AishikRakshit_190122002.ipynb
new file mode 100644
index 000000000..135e1495e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/AishikRakshit_190122002.ipynb	
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from matplotlib import pyplot as plt\n",
+    "from numpy.linalg import inv\n",
+    "from mpl_toolkits import mplot3d as plt3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds1=np.genfromtxt(r\"C:\\Users\\LENOVO\\Desktop\\machine-learning-ex\\ex1\\ex1data1.csv\",delimiter=',')\n",
+    "ds2=np.genfromtxt(r\"C:\\Users\\LENOVO\\Desktop\\machine-learning-ex\\ex1\\ex1data2.csv\",delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotdata():\n",
+    "    print(\"Dataset 1\")\n",
+    "    print(ds1)\n",
+    "    print(\"Dataset 2\")\n",
+    "    print(ds2)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.1101 , 17.592  ],\n",
+       "       [ 5.5277 ,  9.1302 ],\n",
+       "       [ 8.5186 , 13.662  ],\n",
+       "       [ 7.0032 , 11.854  ],\n",
+       "       [ 5.8598 ,  6.8233 ],\n",
+       "       [ 8.3829 , 11.886  ],\n",
+       "       [ 7.4764 ,  4.3483 ],\n",
+       "       [ 8.5781 , 12.     ],\n",
+       "       [ 6.4862 ,  6.5987 ],\n",
+       "       [ 5.0546 ,  3.8166 ],\n",
+       "       [ 5.7107 ,  3.2522 ],\n",
+       "       [14.164  , 15.505  ],\n",
+       "       [ 5.734  ,  3.1551 ],\n",
+       "       [ 8.4084 ,  7.2258 ],\n",
+       "       [ 5.6407 ,  0.71618],\n",
+       "       [ 5.3794 ,  3.5129 ],\n",
+       "       [ 6.3654 ,  5.3048 ],\n",
+       "       [ 5.1301 ,  0.56077],\n",
+       "       [ 6.4296 ,  3.6518 ],\n",
+       "       [ 7.0708 ,  5.3893 ],\n",
+       "       [ 6.1891 ,  3.1386 ],\n",
+       "       [20.27   , 21.767  ],\n",
+       "       [ 5.4901 ,  4.263  ],\n",
+       "       [ 6.3261 ,  5.1875 ],\n",
+       "       [ 5.5649 ,  3.0825 ],\n",
+       "       [18.945  , 22.638  ],\n",
+       "       [12.828  , 13.501  ],\n",
+       "       [10.957  ,  7.0467 ],\n",
+       "       [13.176  , 14.692  ],\n",
+       "       [22.203  , 24.147  ],\n",
+       "       [ 5.2524 , -1.22   ],\n",
+       "       [ 6.5894 ,  5.9966 ],\n",
+       "       [ 9.2482 , 12.134  ],\n",
+       "       [ 5.8918 ,  1.8495 ],\n",
+       "       [ 8.2111 ,  6.5426 ],\n",
+       "       [ 7.9334 ,  4.5623 ],\n",
+       "       [ 8.0959 ,  4.1164 ],\n",
+       "       [ 5.6063 ,  3.3928 ],\n",
+       "       [12.836  , 10.117  ],\n",
+       "       [ 6.3534 ,  5.4974 ],\n",
+       "       [ 5.4069 ,  0.55657],\n",
+       "       [ 6.8825 ,  3.9115 ],\n",
+       "       [11.708  ,  5.3854 ],\n",
+       "       [ 5.7737 ,  2.4406 ],\n",
+       "       [ 7.8247 ,  6.7318 ],\n",
+       "       [ 7.0931 ,  1.0463 ],\n",
+       "       [ 5.0702 ,  5.1337 ],\n",
+       "       [ 5.8014 ,  1.844  ],\n",
+       "       [11.7    ,  8.0043 ],\n",
+       "       [ 5.5416 ,  1.0179 ],\n",
+       "       [ 7.5402 ,  6.7504 ],\n",
+       "       [ 5.3077 ,  1.8396 ],\n",
+       "       [ 7.4239 ,  4.2885 ],\n",
+       "       [ 7.6031 ,  4.9981 ],\n",
+       "       [ 6.3328 ,  1.4233 ],\n",
+       "       [ 6.3589 , -1.4211 ],\n",
+       "       [ 6.2742 ,  2.4756 ],\n",
+       "       [ 5.6397 ,  4.6042 ],\n",
+       "       [ 9.3102 ,  3.9624 ],\n",
+       "       [ 9.4536 ,  5.4141 ],\n",
+       "       [ 8.8254 ,  5.1694 ],\n",
+       "       [ 5.1793 , -0.74279],\n",
+       "       [21.279  , 17.929  ],\n",
+       "       [14.908  , 12.054  ],\n",
+       "       [18.959  , 17.054  ],\n",
+       "       [ 7.2182 ,  4.8852 ],\n",
+       "       [ 8.2951 ,  5.7442 ],\n",
+       "       [10.236  ,  7.7754 ],\n",
+       "       [ 5.4994 ,  1.0173 ],\n",
+       "       [20.341  , 20.992  ],\n",
+       "       [10.136  ,  6.6799 ],\n",
+       "       [ 7.3345 ,  4.0259 ],\n",
+       "       [ 6.0062 ,  1.2784 ],\n",
+       "       [ 7.2259 ,  3.3411 ],\n",
+       "       [ 5.0269 , -2.6807 ],\n",
+       "       [ 6.5479 ,  0.29678],\n",
+       "       [ 7.5386 ,  3.8845 ],\n",
+       "       [ 5.0365 ,  5.7014 ],\n",
+       "       [10.274  ,  6.7526 ],\n",
+       "       [ 5.1077 ,  2.0576 ],\n",
+       "       [ 5.7292 ,  0.47953],\n",
+       "       [ 5.1884 ,  0.20421],\n",
+       "       [ 6.3557 ,  0.67861],\n",
+       "       [ 9.7687 ,  7.5435 ],\n",
+       "       [ 6.5159 ,  5.3436 ],\n",
+       "       [ 8.5172 ,  4.2415 ],\n",
+       "       [ 9.1802 ,  6.7981 ],\n",
+       "       [ 6.002  ,  0.92695],\n",
+       "       [ 5.5204 ,  0.152  ],\n",
+       "       [ 5.0594 ,  2.8214 ],\n",
+       "       [ 5.7077 ,  1.8451 ],\n",
+       "       [ 7.6366 ,  4.2959 ],\n",
+       "       [ 5.8707 ,  7.2029 ],\n",
+       "       [ 5.3054 ,  1.9869 ],\n",
+       "       [ 8.2934 ,  0.14454],\n",
+       "       [13.394  ,  9.0551 ],\n",
+       "       [ 5.4369 ,  0.61705]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalEqn(arr):\n",
+    "    \n",
+    "    r=arr.shape[0]\n",
+    "    c=arr.shape[1]\n",
+    "    \n",
+    "    d1=arr[:,c-1]\n",
+    "    \n",
+    "    one1=np.ones((r,1))\n",
+    "    arr=np.concatenate((one1,arr),axis=1)\n",
+    "    \n",
+    "    t_t=arr[:,0:c].transpose()\n",
+    "    t_c=arr[:,0:c]\n",
+    "    a1=inv(np.dot(t_t,t_c))\n",
+    "    b1=np.dot(a1,t_t)\n",
+    "    theta=np.dot(b1,d1)\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[89597.9095428    139.21067402 -8738.01911233]\n"
+     ]
+    }
+   ],
+   "source": [
+    "theta1=normalEqn(ds1)\n",
+    "theta2=normalEqn(ds2)\n",
+    "print(theta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.10400e+03, 3.00000e+00, 3.99900e+05],\n",
+       "       [1.60000e+03, 3.00000e+00, 3.29900e+05],\n",
+       "       [2.40000e+03, 3.00000e+00, 3.69000e+05],\n",
+       "       [1.41600e+03, 2.00000e+00, 2.32000e+05],\n",
+       "       [3.00000e+03, 4.00000e+00, 5.39900e+05],\n",
+       "       [1.98500e+03, 4.00000e+00, 2.99900e+05],\n",
+       "       [1.53400e+03, 3.00000e+00, 3.14900e+05],\n",
+       "       [1.42700e+03, 3.00000e+00, 1.98999e+05],\n",
+       "       [1.38000e+03, 3.00000e+00, 2.12000e+05],\n",
+       "       [1.49400e+03, 3.00000e+00, 2.42500e+05],\n",
+       "       [1.94000e+03, 4.00000e+00, 2.39999e+05],\n",
+       "       [2.00000e+03, 3.00000e+00, 3.47000e+05],\n",
+       "       [1.89000e+03, 3.00000e+00, 3.29999e+05],\n",
+       "       [4.47800e+03, 5.00000e+00, 6.99900e+05],\n",
+       "       [1.26800e+03, 3.00000e+00, 2.59900e+05],\n",
+       "       [2.30000e+03, 4.00000e+00, 4.49900e+05],\n",
+       "       [1.32000e+03, 2.00000e+00, 2.99900e+05],\n",
+       "       [1.23600e+03, 3.00000e+00, 1.99900e+05],\n",
+       "       [2.60900e+03, 4.00000e+00, 4.99998e+05],\n",
+       "       [3.03100e+03, 4.00000e+00, 5.99000e+05],\n",
+       "       [1.76700e+03, 3.00000e+00, 2.52900e+05],\n",
+       "       [1.88800e+03, 2.00000e+00, 2.55000e+05],\n",
+       "       [1.60400e+03, 3.00000e+00, 2.42900e+05],\n",
+       "       [1.96200e+03, 4.00000e+00, 2.59900e+05],\n",
+       "       [3.89000e+03, 3.00000e+00, 5.73900e+05],\n",
+       "       [1.10000e+03, 3.00000e+00, 2.49900e+05],\n",
+       "       [1.45800e+03, 3.00000e+00, 4.64500e+05],\n",
+       "       [2.52600e+03, 3.00000e+00, 4.69000e+05],\n",
+       "       [2.20000e+03, 3.00000e+00, 4.75000e+05],\n",
+       "       [2.63700e+03, 3.00000e+00, 2.99900e+05],\n",
+       "       [1.83900e+03, 2.00000e+00, 3.49900e+05],\n",
+       "       [1.00000e+03, 1.00000e+00, 1.69900e+05],\n",
+       "       [2.04000e+03, 4.00000e+00, 3.14900e+05],\n",
+       "       [3.13700e+03, 3.00000e+00, 5.79900e+05],\n",
+       "       [1.81100e+03, 4.00000e+00, 2.85900e+05],\n",
+       "       [1.43700e+03, 3.00000e+00, 2.49900e+05],\n",
+       "       [1.23900e+03, 3.00000e+00, 2.29900e+05],\n",
+       "       [2.13200e+03, 4.00000e+00, 3.45000e+05],\n",
+       "       [4.21500e+03, 4.00000e+00, 5.49000e+05],\n",
+       "       [2.16200e+03, 4.00000e+00, 2.87000e+05],\n",
+       "       [1.66400e+03, 2.00000e+00, 3.68500e+05],\n",
+       "       [2.23800e+03, 3.00000e+00, 3.29900e+05],\n",
+       "       [2.56700e+03, 4.00000e+00, 3.14000e+05],\n",
+       "       [1.20000e+03, 3.00000e+00, 2.99000e+05],\n",
+       "       [8.52000e+02, 2.00000e+00, 1.79900e+05],\n",
+       "       [1.85200e+03, 4.00000e+00, 2.99900e+05],\n",
+       "       [1.20300e+03, 3.00000e+00, 2.39500e+05]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds1\n",
+    "ds2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCost(theta,arr):\n",
+    "    r=arr.shape[0]\n",
+    "    c=arr.shape[1]\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    one1=np.ones((r,1))\n",
+    "    arr=np.concatenate((one1,arr),axis=1)\n",
+    "    theta_t=theta.transpose()\n",
+    "    cost=np.dot(arr[:,0:c],theta_t)\n",
+    "    return cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCostMulti(theta,arr):\n",
+    "    r=arr.shape[0]\n",
+    "    c=arr.shape[1] \n",
+    "    one1=np.ones((r,1))\n",
+    "    arr=np.concatenate((one1,arr),axis=1)\n",
+    "    theta_t=theta.transpose()\n",
+    "    cost=np.dot(arr[:,0:c],theta_t)\n",
+    "    return cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cost1=computeCost(theta1,ds1)\n",
+    "cost2=computeCostMulti(theta2,ds2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.39377399  2.6989512   6.26719552  4.45927234  3.09515767  6.10530086\n",
+      "  5.02381586  6.33818102  3.84247394  2.13452698  2.91727635 13.00234766\n",
+      "  2.94507404  6.13572322  2.833764    2.52202431  3.69835548  2.22460102\n",
+      "  3.77494824  4.53992141  3.48802365 20.28701109  2.65409313  3.65146926\n",
+      "  2.74333205 18.70624151 11.40845471  9.17628876 11.82363042 22.59314512\n",
+      "  2.37050903  3.96559502  7.13763287  3.13333475  5.90033768  5.56903223\n",
+      "  5.7629002   2.79272364 11.41799898  3.68403908  2.55483273  4.31527318\n",
+      " 10.07225703  2.99243747  5.43934948  4.56652606  2.1531383   3.02548451\n",
+      " 10.06271276  2.71553436  5.09993141  2.43648379  4.96118159  5.17497322\n",
+      "  3.65946258  3.69060076  3.58955081  2.83257096  7.21160096  7.38268198\n",
+      "  6.63321825  2.28329828 21.49078204 13.88996469 18.72294398  4.71577457\n",
+      "  6.0005525   8.3161115   2.66518834 20.37171648  8.19680814  4.85452438\n",
+      "  3.2698178   4.72496093  2.10147995  3.91608412  5.09802255  2.11293307\n",
+      "  8.36144678  2.19787707  2.93934748  2.29415488  3.68678305  7.75860688\n",
+      "  3.87790704  6.26552528  7.05650658  3.26480705  2.69024205  2.14025354\n",
+      "  2.91369725  5.21493985  3.10816174  2.43373982  5.99852435 12.08371175\n",
+      "  2.59062374]\n",
+      "[356283.1103389  286120.93063401 397489.46984811 269244.1857271\n",
+      " 472277.85514636 330979.02101847 276933.02614885 262037.48402896\n",
+      " 255494.58235014 271364.59918814 324714.54068768 341805.20024106\n",
+      " 326492.02609912 669293.21223208 239902.98686016 374830.38333402\n",
+      " 255879.96102141 235448.2452916  417846.48160547 476593.38604091\n",
+      " 309369.11319496 334951.62386342 286677.77333008 327777.17551607\n",
+      " 604913.37413438 216515.5936252  266353.01492351 415030.01477433\n",
+      " 369647.33504459 430482.39959029 328130.30083655 220070.56444809\n",
+      " 338635.60808944 500087.7365991  306756.3637394  263429.59076914\n",
+      " 235865.87731365 351442.99009906 641418.82407778 355619.31031959\n",
+      " 303768.43288347 374937.34065726 411999.63329673 230436.66102696\n",
+      " 190729.36558116 312464.00137413 230854.29304902]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cost1)\n",
+    "print(cost2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    },
+    {
+     "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": [
+    "r=ds1.shape[0]\n",
+    "c=ds1.shape[1]\n",
+    "print (c)\n",
+    "plt.scatter(ds1[:,0],ds1[:,1])\n",
+    "plt.plot(ds1[:,0],cost1,'r')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def featureNormalize(arr):\n",
+    "    c=arr.shape[1]\n",
+    "    for i in range(0,c):\n",
+    "        rng=max(arr[:,i])-min(arr[:,i])\n",
+    "        arr[:,i]=arr[:,i]/rng\n",
+    "    return arr\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r1=ds1.shape[0]\n",
+    "c1=ds1.shape[1] \n",
+    "one1=np.ones((r1,1))\n",
+    "ds_1=featureNormalize(ds1[:,0:c1])\n",
+    "ds_1n=np.concatenate((one1,ds_1),axis=1)\n",
+    "r2=ds2.shape[0]\n",
+    "c2=ds2.shape[1] \n",
+    "one2=np.ones((r2,1))\n",
+    "ds_2=featureNormalize(ds2[:,0:c2])\n",
+    "ds_2n=np.concatenate((one2,ds_2),axis=1)\n",
+    "\n",
+    "x1=ds_1n[:,0:c1+1]\n",
+    "x2=ds_2n[:,0:c2+1]\n",
+    "y1=ds_1n[:,c1]\n",
+    "y2=ds_2n[:,c2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientMulti(X_norm,y,theta,alpha,m,n):\n",
+    "    temp=np.array(np.zeros_like(theta,float))\n",
+    "    h=computeCost(theta,X_norm)\n",
+    "    while(j>0.01):\n",
+    "        for i in range(0,n):\n",
+    "            temp[i]=theta[i]-(alpha/m)*(np.sum((h-y)*X_norm[:,i]))\n",
+    "        theta=temp\n",
+    "        h=computeCost(theta,X_norm)\n",
+    "        j=(1/(2*m))*np.sum(np.square(h-y))  \n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradient(X_norm,y,theta,alpha,m,n,num_it):\n",
+    "    temp=np.array(np.zeros_like(theta,float))\n",
+    "    h=computeCost(theta,X_norm)\n",
+    "    while(j>0.01):\n",
+    "        temp[0]=theta[0]-(alpha/m)*(np.sum(h-y))\n",
+    "        temp[1]=theta[1]-(alpha/m)*(np.sum((h-y)*X_norm[:,1]))\n",
+    "        theta=temp\n",
+    "        h=computeCost(theta,X_norm)\n",
+    "        j=(1/(2*m))*np.sum(np.square(h-y))\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/AishikRakshit_190122002.ipynb b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/AishikRakshit_190122002.ipynb
new file mode 100644
index 000000000..135e1495e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/AishikRakshit_190122002.ipynb	
@@ -0,0 +1,509 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import os\n",
+    "from matplotlib import pyplot as plt\n",
+    "from numpy.linalg import inv\n",
+    "from mpl_toolkits import mplot3d as plt3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds1=np.genfromtxt(r\"C:\\Users\\LENOVO\\Desktop\\machine-learning-ex\\ex1\\ex1data1.csv\",delimiter=',')\n",
+    "ds2=np.genfromtxt(r\"C:\\Users\\LENOVO\\Desktop\\machine-learning-ex\\ex1\\ex1data2.csv\",delimiter=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotdata():\n",
+    "    print(\"Dataset 1\")\n",
+    "    print(ds1)\n",
+    "    print(\"Dataset 2\")\n",
+    "    print(ds2)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 6.1101 , 17.592  ],\n",
+       "       [ 5.5277 ,  9.1302 ],\n",
+       "       [ 8.5186 , 13.662  ],\n",
+       "       [ 7.0032 , 11.854  ],\n",
+       "       [ 5.8598 ,  6.8233 ],\n",
+       "       [ 8.3829 , 11.886  ],\n",
+       "       [ 7.4764 ,  4.3483 ],\n",
+       "       [ 8.5781 , 12.     ],\n",
+       "       [ 6.4862 ,  6.5987 ],\n",
+       "       [ 5.0546 ,  3.8166 ],\n",
+       "       [ 5.7107 ,  3.2522 ],\n",
+       "       [14.164  , 15.505  ],\n",
+       "       [ 5.734  ,  3.1551 ],\n",
+       "       [ 8.4084 ,  7.2258 ],\n",
+       "       [ 5.6407 ,  0.71618],\n",
+       "       [ 5.3794 ,  3.5129 ],\n",
+       "       [ 6.3654 ,  5.3048 ],\n",
+       "       [ 5.1301 ,  0.56077],\n",
+       "       [ 6.4296 ,  3.6518 ],\n",
+       "       [ 7.0708 ,  5.3893 ],\n",
+       "       [ 6.1891 ,  3.1386 ],\n",
+       "       [20.27   , 21.767  ],\n",
+       "       [ 5.4901 ,  4.263  ],\n",
+       "       [ 6.3261 ,  5.1875 ],\n",
+       "       [ 5.5649 ,  3.0825 ],\n",
+       "       [18.945  , 22.638  ],\n",
+       "       [12.828  , 13.501  ],\n",
+       "       [10.957  ,  7.0467 ],\n",
+       "       [13.176  , 14.692  ],\n",
+       "       [22.203  , 24.147  ],\n",
+       "       [ 5.2524 , -1.22   ],\n",
+       "       [ 6.5894 ,  5.9966 ],\n",
+       "       [ 9.2482 , 12.134  ],\n",
+       "       [ 5.8918 ,  1.8495 ],\n",
+       "       [ 8.2111 ,  6.5426 ],\n",
+       "       [ 7.9334 ,  4.5623 ],\n",
+       "       [ 8.0959 ,  4.1164 ],\n",
+       "       [ 5.6063 ,  3.3928 ],\n",
+       "       [12.836  , 10.117  ],\n",
+       "       [ 6.3534 ,  5.4974 ],\n",
+       "       [ 5.4069 ,  0.55657],\n",
+       "       [ 6.8825 ,  3.9115 ],\n",
+       "       [11.708  ,  5.3854 ],\n",
+       "       [ 5.7737 ,  2.4406 ],\n",
+       "       [ 7.8247 ,  6.7318 ],\n",
+       "       [ 7.0931 ,  1.0463 ],\n",
+       "       [ 5.0702 ,  5.1337 ],\n",
+       "       [ 5.8014 ,  1.844  ],\n",
+       "       [11.7    ,  8.0043 ],\n",
+       "       [ 5.5416 ,  1.0179 ],\n",
+       "       [ 7.5402 ,  6.7504 ],\n",
+       "       [ 5.3077 ,  1.8396 ],\n",
+       "       [ 7.4239 ,  4.2885 ],\n",
+       "       [ 7.6031 ,  4.9981 ],\n",
+       "       [ 6.3328 ,  1.4233 ],\n",
+       "       [ 6.3589 , -1.4211 ],\n",
+       "       [ 6.2742 ,  2.4756 ],\n",
+       "       [ 5.6397 ,  4.6042 ],\n",
+       "       [ 9.3102 ,  3.9624 ],\n",
+       "       [ 9.4536 ,  5.4141 ],\n",
+       "       [ 8.8254 ,  5.1694 ],\n",
+       "       [ 5.1793 , -0.74279],\n",
+       "       [21.279  , 17.929  ],\n",
+       "       [14.908  , 12.054  ],\n",
+       "       [18.959  , 17.054  ],\n",
+       "       [ 7.2182 ,  4.8852 ],\n",
+       "       [ 8.2951 ,  5.7442 ],\n",
+       "       [10.236  ,  7.7754 ],\n",
+       "       [ 5.4994 ,  1.0173 ],\n",
+       "       [20.341  , 20.992  ],\n",
+       "       [10.136  ,  6.6799 ],\n",
+       "       [ 7.3345 ,  4.0259 ],\n",
+       "       [ 6.0062 ,  1.2784 ],\n",
+       "       [ 7.2259 ,  3.3411 ],\n",
+       "       [ 5.0269 , -2.6807 ],\n",
+       "       [ 6.5479 ,  0.29678],\n",
+       "       [ 7.5386 ,  3.8845 ],\n",
+       "       [ 5.0365 ,  5.7014 ],\n",
+       "       [10.274  ,  6.7526 ],\n",
+       "       [ 5.1077 ,  2.0576 ],\n",
+       "       [ 5.7292 ,  0.47953],\n",
+       "       [ 5.1884 ,  0.20421],\n",
+       "       [ 6.3557 ,  0.67861],\n",
+       "       [ 9.7687 ,  7.5435 ],\n",
+       "       [ 6.5159 ,  5.3436 ],\n",
+       "       [ 8.5172 ,  4.2415 ],\n",
+       "       [ 9.1802 ,  6.7981 ],\n",
+       "       [ 6.002  ,  0.92695],\n",
+       "       [ 5.5204 ,  0.152  ],\n",
+       "       [ 5.0594 ,  2.8214 ],\n",
+       "       [ 5.7077 ,  1.8451 ],\n",
+       "       [ 7.6366 ,  4.2959 ],\n",
+       "       [ 5.8707 ,  7.2029 ],\n",
+       "       [ 5.3054 ,  1.9869 ],\n",
+       "       [ 8.2934 ,  0.14454],\n",
+       "       [13.394  ,  9.0551 ],\n",
+       "       [ 5.4369 ,  0.61705]])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def normalEqn(arr):\n",
+    "    \n",
+    "    r=arr.shape[0]\n",
+    "    c=arr.shape[1]\n",
+    "    \n",
+    "    d1=arr[:,c-1]\n",
+    "    \n",
+    "    one1=np.ones((r,1))\n",
+    "    arr=np.concatenate((one1,arr),axis=1)\n",
+    "    \n",
+    "    t_t=arr[:,0:c].transpose()\n",
+    "    t_c=arr[:,0:c]\n",
+    "    a1=inv(np.dot(t_t,t_c))\n",
+    "    b1=np.dot(a1,t_t)\n",
+    "    theta=np.dot(b1,d1)\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[89597.9095428    139.21067402 -8738.01911233]\n"
+     ]
+    }
+   ],
+   "source": [
+    "theta1=normalEqn(ds1)\n",
+    "theta2=normalEqn(ds2)\n",
+    "print(theta2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2.10400e+03, 3.00000e+00, 3.99900e+05],\n",
+       "       [1.60000e+03, 3.00000e+00, 3.29900e+05],\n",
+       "       [2.40000e+03, 3.00000e+00, 3.69000e+05],\n",
+       "       [1.41600e+03, 2.00000e+00, 2.32000e+05],\n",
+       "       [3.00000e+03, 4.00000e+00, 5.39900e+05],\n",
+       "       [1.98500e+03, 4.00000e+00, 2.99900e+05],\n",
+       "       [1.53400e+03, 3.00000e+00, 3.14900e+05],\n",
+       "       [1.42700e+03, 3.00000e+00, 1.98999e+05],\n",
+       "       [1.38000e+03, 3.00000e+00, 2.12000e+05],\n",
+       "       [1.49400e+03, 3.00000e+00, 2.42500e+05],\n",
+       "       [1.94000e+03, 4.00000e+00, 2.39999e+05],\n",
+       "       [2.00000e+03, 3.00000e+00, 3.47000e+05],\n",
+       "       [1.89000e+03, 3.00000e+00, 3.29999e+05],\n",
+       "       [4.47800e+03, 5.00000e+00, 6.99900e+05],\n",
+       "       [1.26800e+03, 3.00000e+00, 2.59900e+05],\n",
+       "       [2.30000e+03, 4.00000e+00, 4.49900e+05],\n",
+       "       [1.32000e+03, 2.00000e+00, 2.99900e+05],\n",
+       "       [1.23600e+03, 3.00000e+00, 1.99900e+05],\n",
+       "       [2.60900e+03, 4.00000e+00, 4.99998e+05],\n",
+       "       [3.03100e+03, 4.00000e+00, 5.99000e+05],\n",
+       "       [1.76700e+03, 3.00000e+00, 2.52900e+05],\n",
+       "       [1.88800e+03, 2.00000e+00, 2.55000e+05],\n",
+       "       [1.60400e+03, 3.00000e+00, 2.42900e+05],\n",
+       "       [1.96200e+03, 4.00000e+00, 2.59900e+05],\n",
+       "       [3.89000e+03, 3.00000e+00, 5.73900e+05],\n",
+       "       [1.10000e+03, 3.00000e+00, 2.49900e+05],\n",
+       "       [1.45800e+03, 3.00000e+00, 4.64500e+05],\n",
+       "       [2.52600e+03, 3.00000e+00, 4.69000e+05],\n",
+       "       [2.20000e+03, 3.00000e+00, 4.75000e+05],\n",
+       "       [2.63700e+03, 3.00000e+00, 2.99900e+05],\n",
+       "       [1.83900e+03, 2.00000e+00, 3.49900e+05],\n",
+       "       [1.00000e+03, 1.00000e+00, 1.69900e+05],\n",
+       "       [2.04000e+03, 4.00000e+00, 3.14900e+05],\n",
+       "       [3.13700e+03, 3.00000e+00, 5.79900e+05],\n",
+       "       [1.81100e+03, 4.00000e+00, 2.85900e+05],\n",
+       "       [1.43700e+03, 3.00000e+00, 2.49900e+05],\n",
+       "       [1.23900e+03, 3.00000e+00, 2.29900e+05],\n",
+       "       [2.13200e+03, 4.00000e+00, 3.45000e+05],\n",
+       "       [4.21500e+03, 4.00000e+00, 5.49000e+05],\n",
+       "       [2.16200e+03, 4.00000e+00, 2.87000e+05],\n",
+       "       [1.66400e+03, 2.00000e+00, 3.68500e+05],\n",
+       "       [2.23800e+03, 3.00000e+00, 3.29900e+05],\n",
+       "       [2.56700e+03, 4.00000e+00, 3.14000e+05],\n",
+       "       [1.20000e+03, 3.00000e+00, 2.99000e+05],\n",
+       "       [8.52000e+02, 2.00000e+00, 1.79900e+05],\n",
+       "       [1.85200e+03, 4.00000e+00, 2.99900e+05],\n",
+       "       [1.20300e+03, 3.00000e+00, 2.39500e+05]])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds1\n",
+    "ds2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCost(theta,arr):\n",
+    "    r=arr.shape[0]\n",
+    "    c=arr.shape[1]\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    one1=np.ones((r,1))\n",
+    "    arr=np.concatenate((one1,arr),axis=1)\n",
+    "    theta_t=theta.transpose()\n",
+    "    cost=np.dot(arr[:,0:c],theta_t)\n",
+    "    return cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def computeCostMulti(theta,arr):\n",
+    "    r=arr.shape[0]\n",
+    "    c=arr.shape[1] \n",
+    "    one1=np.ones((r,1))\n",
+    "    arr=np.concatenate((one1,arr),axis=1)\n",
+    "    theta_t=theta.transpose()\n",
+    "    cost=np.dot(arr[:,0:c],theta_t)\n",
+    "    return cost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cost1=computeCost(theta1,ds1)\n",
+    "cost2=computeCostMulti(theta2,ds2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 3.39377399  2.6989512   6.26719552  4.45927234  3.09515767  6.10530086\n",
+      "  5.02381586  6.33818102  3.84247394  2.13452698  2.91727635 13.00234766\n",
+      "  2.94507404  6.13572322  2.833764    2.52202431  3.69835548  2.22460102\n",
+      "  3.77494824  4.53992141  3.48802365 20.28701109  2.65409313  3.65146926\n",
+      "  2.74333205 18.70624151 11.40845471  9.17628876 11.82363042 22.59314512\n",
+      "  2.37050903  3.96559502  7.13763287  3.13333475  5.90033768  5.56903223\n",
+      "  5.7629002   2.79272364 11.41799898  3.68403908  2.55483273  4.31527318\n",
+      " 10.07225703  2.99243747  5.43934948  4.56652606  2.1531383   3.02548451\n",
+      " 10.06271276  2.71553436  5.09993141  2.43648379  4.96118159  5.17497322\n",
+      "  3.65946258  3.69060076  3.58955081  2.83257096  7.21160096  7.38268198\n",
+      "  6.63321825  2.28329828 21.49078204 13.88996469 18.72294398  4.71577457\n",
+      "  6.0005525   8.3161115   2.66518834 20.37171648  8.19680814  4.85452438\n",
+      "  3.2698178   4.72496093  2.10147995  3.91608412  5.09802255  2.11293307\n",
+      "  8.36144678  2.19787707  2.93934748  2.29415488  3.68678305  7.75860688\n",
+      "  3.87790704  6.26552528  7.05650658  3.26480705  2.69024205  2.14025354\n",
+      "  2.91369725  5.21493985  3.10816174  2.43373982  5.99852435 12.08371175\n",
+      "  2.59062374]\n",
+      "[356283.1103389  286120.93063401 397489.46984811 269244.1857271\n",
+      " 472277.85514636 330979.02101847 276933.02614885 262037.48402896\n",
+      " 255494.58235014 271364.59918814 324714.54068768 341805.20024106\n",
+      " 326492.02609912 669293.21223208 239902.98686016 374830.38333402\n",
+      " 255879.96102141 235448.2452916  417846.48160547 476593.38604091\n",
+      " 309369.11319496 334951.62386342 286677.77333008 327777.17551607\n",
+      " 604913.37413438 216515.5936252  266353.01492351 415030.01477433\n",
+      " 369647.33504459 430482.39959029 328130.30083655 220070.56444809\n",
+      " 338635.60808944 500087.7365991  306756.3637394  263429.59076914\n",
+      " 235865.87731365 351442.99009906 641418.82407778 355619.31031959\n",
+      " 303768.43288347 374937.34065726 411999.63329673 230436.66102696\n",
+      " 190729.36558116 312464.00137413 230854.29304902]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(cost1)\n",
+    "print(cost2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    },
+    {
+     "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": [
+    "r=ds1.shape[0]\n",
+    "c=ds1.shape[1]\n",
+    "print (c)\n",
+    "plt.scatter(ds1[:,0],ds1[:,1])\n",
+    "plt.plot(ds1[:,0],cost1,'r')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def featureNormalize(arr):\n",
+    "    c=arr.shape[1]\n",
+    "    for i in range(0,c):\n",
+    "        rng=max(arr[:,i])-min(arr[:,i])\n",
+    "        arr[:,i]=arr[:,i]/rng\n",
+    "    return arr\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r1=ds1.shape[0]\n",
+    "c1=ds1.shape[1] \n",
+    "one1=np.ones((r1,1))\n",
+    "ds_1=featureNormalize(ds1[:,0:c1])\n",
+    "ds_1n=np.concatenate((one1,ds_1),axis=1)\n",
+    "r2=ds2.shape[0]\n",
+    "c2=ds2.shape[1] \n",
+    "one2=np.ones((r2,1))\n",
+    "ds_2=featureNormalize(ds2[:,0:c2])\n",
+    "ds_2n=np.concatenate((one2,ds_2),axis=1)\n",
+    "\n",
+    "x1=ds_1n[:,0:c1+1]\n",
+    "x2=ds_2n[:,0:c2+1]\n",
+    "y1=ds_1n[:,c1]\n",
+    "y2=ds_2n[:,c2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradientMulti(X_norm,y,theta,alpha,m,n):\n",
+    "    temp=np.array(np.zeros_like(theta,float))\n",
+    "    h=computeCost(theta,X_norm)\n",
+    "    while(j>0.01):\n",
+    "        for i in range(0,n):\n",
+    "            temp[i]=theta[i]-(alpha/m)*(np.sum((h-y)*X_norm[:,i]))\n",
+    "        theta=temp\n",
+    "        h=computeCost(theta,X_norm)\n",
+    "        j=(1/(2*m))*np.sum(np.square(h-y))  \n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradient(X_norm,y,theta,alpha,m,n,num_it):\n",
+    "    temp=np.array(np.zeros_like(theta,float))\n",
+    "    h=computeCost(theta,X_norm)\n",
+    "    while(j>0.01):\n",
+    "        temp[0]=theta[0]-(alpha/m)*(np.sum(h-y))\n",
+    "        temp[1]=theta[1]-(alpha/m)*(np.sum((h-y)*X_norm[:,1]))\n",
+    "        theta=temp\n",
+    "        h=computeCost(theta,X_norm)\n",
+    "        j=(1/(2*m))*np.sum(np.square(h-y))\n",
+    "    return theta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/ex1data1.csv b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/ex1data1.csv
new file mode 100644
index 000000000..0f88ccb61
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/ex1data1.csv	
@@ -0,0 +1,97 @@
+6.1101,17.592
+5.5277,9.1302
+8.5186,13.662
+7.0032,11.854
+5.8598,6.8233
+8.3829,11.886
+7.4764,4.3483
+8.5781,12
+6.4862,6.5987
+5.0546,3.8166
+5.7107,3.2522
+14.164,15.505
+5.734,3.1551
+8.4084,7.2258
+5.6407,0.71618
+5.3794,3.5129
+6.3654,5.3048
+5.1301,0.56077
+6.4296,3.6518
+7.0708,5.3893
+6.1891,3.1386
+20.27,21.767
+5.4901,4.263
+6.3261,5.1875
+5.5649,3.0825
+18.945,22.638
+12.828,13.501
+10.957,7.0467
+13.176,14.692
+22.203,24.147
+5.2524,-1.22
+6.5894,5.9966
+9.2482,12.134
+5.8918,1.8495
+8.2111,6.5426
+7.9334,4.5623
+8.0959,4.1164
+5.6063,3.3928
+12.836,10.117
+6.3534,5.4974
+5.4069,0.55657
+6.8825,3.9115
+11.708,5.3854
+5.7737,2.4406
+7.8247,6.7318
+7.0931,1.0463
+5.0702,5.1337
+5.8014,1.844
+11.7,8.0043
+5.5416,1.0179
+7.5402,6.7504
+5.3077,1.8396
+7.4239,4.2885
+7.6031,4.9981
+6.3328,1.4233
+6.3589,-1.4211
+6.2742,2.4756
+5.6397,4.6042
+9.3102,3.9624
+9.4536,5.4141
+8.8254,5.1694
+5.1793,-0.74279
+21.279,17.929
+14.908,12.054
+18.959,17.054
+7.2182,4.8852
+8.2951,5.7442
+10.236,7.7754
+5.4994,1.0173
+20.341,20.992
+10.136,6.6799
+7.3345,4.0259
+6.0062,1.2784
+7.2259,3.3411
+5.0269,-2.6807
+6.5479,0.29678
+7.5386,3.8845
+5.0365,5.7014
+10.274,6.7526
+5.1077,2.0576
+5.7292,0.47953
+5.1884,0.20421
+6.3557,0.67861
+9.7687,7.5435
+6.5159,5.3436
+8.5172,4.2415
+9.1802,6.7981
+6.002,0.92695
+5.5204,0.152
+5.0594,2.8214
+5.7077,1.8451
+7.6366,4.2959
+5.8707,7.2029
+5.3054,1.9869
+8.2934,0.14454
+13.394,9.0551
+5.4369,0.61705
diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/ex1data2.csv b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/ex1data2.csv
new file mode 100644
index 000000000..79e9a807e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/Exercise1/ex1data2.csv	
@@ -0,0 +1,47 @@
+2104,3,399900
+1600,3,329900
+2400,3,369000
+1416,2,232000
+3000,4,539900
+1985,4,299900
+1534,3,314900
+1427,3,198999
+1380,3,212000
+1494,3,242500
+1940,4,239999
+2000,3,347000
+1890,3,329999
+4478,5,699900
+1268,3,259900
+2300,4,449900
+1320,2,299900
+1236,3,199900
+2609,4,499998
+3031,4,599000
+1767,3,252900
+1888,2,255000
+1604,3,242900
+1962,4,259900
+3890,3,573900
+1100,3,249900
+1458,3,464500
+2526,3,469000
+2200,3,475000
+2637,3,299900
+1839,2,349900
+1000,1,169900
+2040,4,314900
+3137,3,579900
+1811,4,285900
+1437,3,249900
+1239,3,229900
+2132,4,345000
+4215,4,549000
+2162,4,287000
+1664,2,368500
+2238,3,329900
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diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1data1.csv b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1data1.csv
new file mode 100644
index 000000000..0f88ccb61
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1data1.csv	
@@ -0,0 +1,97 @@
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diff --git a/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1data2.csv b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1data2.csv
new file mode 100644
index 000000000..79e9a807e
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 2 (Apr 5 - Apr 11)/ex1data2.csv	
@@ -0,0 +1,47 @@
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diff --git a/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/AishikRakshit_190122002_W03.ipynb b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/AishikRakshit_190122002_W03.ipynb
new file mode 100644
index 000000000..3f4faf068
--- /dev/null
+++ b/Phase 3 - 2020 (Summer)/Week 3(Apr 13 - Apr 18)/AishikRakshit_190122002_W03.ipynb	
@@ -0,0 +1,526 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# used for manipulating directory paths\n",
+    "import os\n",
+    "\n",
+    "# Scientific and vector computation for python\n",
+    "import numpy as np\n",
+    "\n",
+    "# Plotting library\n",
+    "from matplotlib import pyplot\n",
+    "\n",
+    "# Optimization module in scipy\n",
+    "from scipy import optimize\n",
+    "\n",
+    "# library written for this exercise providing additional functions for assignment submission, and others\n",
+    "import utils\n",
+    "\n",
+    "\n",
+    "\n",
+    "# tells matplotlib to embed plots within the notebook\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load data\n",
+    "# The first two columns contains the exam scores and the third column\n",
+    "# contains the label.\n",
+    "data = np.loadtxt(r'C:\\Users\\LENOVO\\Desktop\\Machine learning\\machine-learning-ex\\ex2\\ex2data1.txt', delimiter=',')\n",
+    "X, y = data[:, 0:2], data[:, 2]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plotData(X, y):\n",
+    "    \"\"\"\n",
+    "    Plots the data points X and y into a new figure. Plots the data \n",
+    "    points with * for the positive examples and o for the negative examples.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    X : array_like\n",
+    "        An Mx2 matrix representing the dataset. \n",
+    "    \n",
+    "    y : array_like\n",
+    "        Label values for the dataset. A vector of size (M, ).\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Plot the positive and negative examples on a 2D plot, using the\n",
+    "    option 'k*' for the positive examples and 'ko' for the negative examples.    \n",
+    "    \"\"\"\n",
+    "    # Create New Figure\n",
+    "    fig = pyplot.figure()\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    pos = y == 1\n",
+    "    neg = y == 0\n",
+    "    pyplot.plot(X[pos, 0], X[pos, 1], 'k*', lw=2, ms=10)\n",
+    "    pyplot.plot(X[neg, 0], X[neg, 1], 'ko', mfc='y', ms=8, mec='k', mew=1)\n",
+    "    \n",
+    "    \n",
+    "    # ============================================================"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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": [
+    "plotData(X, y)\n",
+    "# add axes labels\n",
+    "pyplot.xlabel('Exam 1 score')\n",
+    "pyplot.ylabel('Exam 2 score')\n",
+    "pyplot.legend(['Admitted', 'Not admitted'])\n",
+    "pass\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sigmoid(z):\n",
+    "    \"\"\"\n",
+    "    Compute sigmoid function given the input z.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    z : array_like\n",
+    "        The input to the sigmoid function. This can be a 1-D vector \n",
+    "        or a 2-D matrix. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    g : array_like\n",
+    "        The computed sigmoid function. g has the same shape as z, since\n",
+    "        the sigmoid is computed element-wise on z.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the sigmoid of each value of z (z can be a matrix, vector or scalar).\n",
+    "    \"\"\"\n",
+    "    # convert input to a numpy array\n",
+    "    z = np.array(z)\n",
+    "    \n",
+    "    # You need to return the following variables correctly \n",
+    "    g = np.zeros(z.shape)\n",
+    "    \n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    g=1/(1+np.exp(-z))\n",
+    "    # =============================================================\n",
+    "    return g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "g( 0 ) =  0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test the implementation of sigmoid function here\n",
+    "z = 0\n",
+    "g = sigmoid(z)\n",
+    "\n",
+    "print('g(', z, ') = ', g)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Setup the data matrix appropriately, and add ones for the intercept term\n",
+    "m, n = X.shape\n",
+    "\n",
+    "# Add intercept term to X\n",
+    "X = np.concatenate([np.ones((m, 1)), X], axis=1)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunction(theta, X, y):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression. \n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        The parameters for logistic regression. This a vector\n",
+    "        of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The input dataset of shape (m x n+1) where m is the total number\n",
+    "        of data points and n is the number of features. We assume the \n",
+    "        intercept has already been added to the input.\n",
+    "    \n",
+    "    y : arra_like\n",
+    "        Labels for the input. This is a vector of shape (m, ).\n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n+1, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "        \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost of a particular choice of theta. You should set J to \n",
+    "    the cost. Compute the partial derivatives and set grad to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    z=np.dot(X,theta)\n",
+    "    A=sigmoid(z)\n",
+    "    J =-1/m * np.sum( np.multiply(np.log(A), y) + np.multiply(np.log(1-A), (1-y)))\n",
+    "    #J=-(1/m)*sum(y*np.log(g)+(1-y)*(1-np.log(g)))\n",
+    "    grad=(1/m)*X.transpose()@(g-y)\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cost\n",
+      "0.6931471805599453\n",
+      "Gradient at initial theta (zeros):\n",
+      "[ -0.1        -12.00921659 -11.26284221]\n",
+      "Cost\n",
+      "0.21833019382659774\n",
+      "Gradient at initial theta (zeros):\n",
+      "[ -0.1        -12.00921659 -11.26284221]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialize fitting parameters\n",
+    "initial_theta = np.zeros(n+1)\n",
+    "\n",
+    "cost, grad = costFunction(initial_theta, X, y)\n",
+    "\n",
+    "print(\"Cost\")\n",
+    "print(cost)\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print(grad)\n",
+    "\n",
+    "test_theta = np.array([-24, 0.2, 0.2])\n",
+    "cost, grad = costFunction(test_theta, X, y)\n",
+    "\n",
+    "print(\"Cost\")\n",
+    "print(cost)\n",
+    "print('Gradient at initial theta (zeros):')\n",
+    "print(grad)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6394269623737789\n",
+      "theta:\n",
+      "[6.39239128e-05 7.67676114e-03 7.19964943e-03]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set options for optimize.minimize\n",
+    "options= {'maxiter': 400}\n",
+    "\n",
+    "# see documention for scipy's optimize.minimize  for description about\n",
+    "# the different parameters\n",
+    "# The function returns an object `OptimizeResult`\n",
+    "# We use truncated Newton algorithm for optimization which is \n",
+    "# equivalent to MATLAB's fminunc\n",
+    "# See https://stackoverflow.com/questions/18801002/fminunc-alternate-in-numpy\n",
+    "res = optimize.minimize(costFunction,\n",
+    "                        initial_theta,\n",
+    "                        (X, y),\n",
+    "                        jac=True,\n",
+    "                        method='TNC',\n",
+    "                        options=options)\n",
+    "# the fun property of `OptimizeResult` object returns\n",
+    "# the value of costFunction at optimized theta\n",
+    "cost = res.fun\n",
+    "\n",
+    "# the optimized theta is in the x property\n",
+    "theta = res.x\n",
+    "print(cost)\n",
+    "print('theta:')\n",
+    "print(theta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict(theta, X):\n",
+    "    \"\"\"\n",
+    "    Predict whether the label is 0 or 1 using learned logistic regression.\n",
+    "    Computes the predictions for X using a threshold at 0.5 \n",
+    "    (i.e., if sigmoid(theta.T*x) >= 0.5, predict 1)\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Parameters for logistic regression. A vecotor of shape (n+1, ).\n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data to use for computing predictions. The rows is the number \n",
+    "        of points to compute predictions, and columns is the number of\n",
+    "        features.\n",
+    "\n",
+    "    Returns\n",
+    "    -------\n",
+    "    p : array_like\n",
+    "        Predictions and 0 or 1 for each row in X. \n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Complete the following code to make predictions using your learned \n",
+    "    logistic regression parameters.You should set p to a vector of 0's and 1's    \n",
+    "    \"\"\"\n",
+    "    m = X.shape[0]\n",
+    "    # Number of training examples\n",
+    "    m\n",
+    "    # You need to return the following variables correctly\n",
+    "    p = np.zeros(m)\n",
+    "\n",
+    "    # ====================== YOUR CODE HERE ======================\n",
+    "    z=X*theta\n",
+    "    h=sigmoid(z)\n",
+    "    p=np.round(p)\n",
+    "        \n",
+    "            \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    # ============================================================\n",
+    "    return p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40.0"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob = sigmoid(np.dot([1, 45, 85], theta))\n",
+    "pr = predict(theta, X)\n",
+    "np.mean(pr == y) * 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Data\n",
+    "# The first two columns contains the X values and the third column\n",
+    "# contains the label (y).\n",
+    "data = np.loadtxt(r'C:\\Users\\LENOVO\\Desktop\\Machine learning\\machine-learning-ex\\ex2\\ex2data2.txt', delimiter=',')\n",
+    "X = data[:, :2]\n",
+    "y = data[:, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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": [
+    "plotData(X, y)\n",
+    "# Labels and Legend\n",
+    "pyplot.xlabel('Microchip Test 1')\n",
+    "pyplot.ylabel('Microchip Test 2')\n",
+    "\n",
+    "# Specified in plot order\n",
+    "pyplot.legend(['y = 1', 'y = 0'], loc='upper right')\n",
+    "pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def costFunctionReg(theta, X, y, Lambda):\n",
+    "    \"\"\"\n",
+    "    Compute cost and gradient for logistic regression with regularization.\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    theta : array_like\n",
+    "        Logistic regression parameters. A vector with shape (n, ). n is \n",
+    "        the number of features including any intercept. If we have mapped\n",
+    "        our initial features into polynomial features, then n is the total \n",
+    "        number of polynomial features. \n",
+    "    \n",
+    "    X : array_like\n",
+    "        The data set with shape (m x n). m is the number of examples, and\n",
+    "        n is the number of features (after feature mapping).\n",
+    "    \n",
+    "    y : array_like\n",
+    "        The data labels. A vector with shape (m, ).\n",
+    "    \n",
+    "    lambda_ : float\n",
+    "        The regularization parameter. \n",
+    "    \n",
+    "    Returns\n",
+    "    -------\n",
+    "    J : float\n",
+    "        The computed value for the regularized cost function. \n",
+    "    \n",
+    "    grad : array_like\n",
+    "        A vector of shape (n, ) which is the gradient of the cost\n",
+    "        function with respect to theta, at the current values of theta.\n",
+    "    \n",
+    "    Instructions\n",
+    "    ------------\n",
+    "    Compute the cost `J` of a particular choice of theta.\n",
+    "    Compute the partial derivatives and set `grad` to the partial\n",
+    "    derivatives of the cost w.r.t. each parameter in theta.\n",
+    "    \"\"\"\n",
+    "    # Initialize some useful values\n",
+    "    m = y.size  # number of training examples\n",
+    "\n",
+    "    # You need to return the following variables correctly \n",
+    "    J = 0\n",
+    "    grad = np.zeros(theta.shape)\n",
+    "\n",
+    "    # ===================== YOUR CODE HERE ======================\n",
+    "    theta2=np.concatenate(0,theta[1:n+1])\n",
+    "    z=np.dot(X,theta)\n",
+    "    A=sigmoid(z)\n",
+    "    J=-1/m * np.sum( np.multiply(np.log(A), y) + np.multiply(np.log(1-A), (1-y)))+(Lambda /(2*m))*sum(theta[1:n+1]^2)\n",
+    "    grad=(1/m)*X.transpose()*(g-y)+(Lambda/m)*sum(theta2[1:n+1])\n",
+    "    \n",
+    "    \n",
+    "    # =============================================================\n",
+    "    return J, grad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
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+ "nbformat_minor": 2
+}