2.2 Linear Regression Step By Step 1D.ipynb

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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = np.loadtxt(\"2data.csv\",delimiter=',')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 2)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Divide the data for linear Regression\n",
    "x = data[:,0]\n",
    "y = data[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((100,), (100,))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape , y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import model_selection\n",
    "X_train, X_test, Y_train, Y_test = model_selection.train_test_split(x,y,test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((70,), (70,))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape , Y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((30,), (30,))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, Y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(x,y):\n",
    "    num = (x*y).mean() - (x.mean())*(y.mean())\n",
    "    den = (x**2).mean() - (x.mean())**2\n",
    "    m = num/den\n",
    "    c = y.mean() - m* (x.mean())\n",
    "    return m,c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost(x,y,m,c):\n",
    "    return ((y- m*x -c)**2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def score (y_truth, y_pred):\n",
    "    u = ((y_truth-y_pred)**2).sum()\n",
    "    v = ((y_truth-y_truth.mean())**2).sum()\n",
    "    return 1 -(u/v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(x,m,c):\n",
    "    return (m*x)+c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Socre:  0.4885388052077798\n",
      "Cost on Testing Data:  118.19426292652118\n",
      "\n",
      "\n",
      "Train Score:  0.6340974068803948\n",
      "Cost on Training Data:  107.26993071640143\n",
      "\n",
      "\n",
      "M,C:  1.3699791327976416 5.941055583979178\n"
     ]
    }
   ],
   "source": [
    "m,c = fit(X_train, Y_train)\n",
    "# test data\n",
    "y_test_pred = predict(X_test,m,c)\n",
    "print(\"Test Socre: \",score(Y_test,y_test_pred))\n",
    "print(\"Cost on Testing Data: \",cost(X_test,Y_test,m,c))\n",
    "print()\n",
    "print()\n",
    "# train data\n",
    "y_train_pred = predict(X_train,m,c)\n",
    "print(\"Train Score: \",score(Y_train, y_train_pred))\n",
    "print(\"Cost on Training Data: \",cost(X_train,Y_train,m,c))\n",
    "\n",
    "print()\n",
    "print()\n",
    "print(\"M,C: \",m,c)"
   ]
  }
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