Upload jithu.txt

jithu.txt

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## K Means Clustering w elbow plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing libraries\n",
+    "from sklearn.cluster import KMeans\n",
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#import dataset\n",
+    "df = pd.read_csv(\"C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/income.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(df.Age, df.Income)\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Income')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaler = MinMaxScaler()\n",
+    "scaler.fit(df[['Income']])\n",
+    "df['Income'] = scaler.transform(df[['Income']])\n",
+    "\n",
+    "scaler.fit(df[['Age']])\n",
+    "df['Age'] = scaler.transform(df[['Age']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Elbow plot\n",
+    "sse = []\n",
+    "k_rng = range(1, 10)\n",
+    "for k in k_rng:\n",
+    "    km = KMeans(n_clusters = k)\n",
+    "    km.fit(df[['Age', 'Income']])\n",
+    "    sse.append(km.inertia_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.xlabel('K')\n",
+    "plt.ylabel('Sum of squared error')\n",
+    "plt.plot(k_rng,sse)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "km = KMeans(n_clusters = 3)\n",
+    "y_predicted = km.fit_predict(df[['Age', 'Income']])\n",
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "km.cluster_centers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['cluster']=y_predicted\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df[df.cluster == 0]\n",
+    "df2 = df[df.cluster == 1]\n",
+    "df3 = df[df.cluster == 2]\n",
+    "\n",
+    "plt.scatter(df1.Age, df1.Income, color = 'black')\n",
+    "plt.scatter(df2.Age, df2.Income, color = 'orange')\n",
+    "plt.scatter(df3.Age, df3.Income, color = 'maroon')\n",
+    "plt.scatter(km.cluster_centers_[:,0], km.cluster_centers_[:,1], color = 'purple', marker = '*', label = 'centroid')\n",
+    "plt.xlabel('Age')\n",
+    "plt.ylabel('Income in $')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Association Rule Mining"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install --no-binary :all: mlxtend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from mlxtend.preprocessing import TransactionEncoder\n",
+    "from mlxtend.frequent_patterns import apriori"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = [[\"Milk\", \"Cola\", \"Beer\"], \n",
+    "          [\"Milk\", \"Pepsi\", \"Juice\"], \n",
+    "          [\"Milk\", \"Beer\"],\n",
+    "          [\"Cola\", \"Juice\"],\n",
+    "          [\"Milk\", \"Pepsi\", \"Beer\"]]\n",
+    "dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var = TransactionEncoder()\n",
+    "var_arr = var.fit(dataset).transform(dataset)\n",
+    "df = pd.DataFrame(var_arr, columns = var.columns_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#from mlxtend.frequent_patterns import apriori\n",
+    "fq_is = apriori(df, min_support = 0.5, use_colnames = True)\n",
+    "fq_is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlxtend.frequent_patterns import association_rules\n",
+    "rules_ap = association_rules(fq_is, metric = \"confidence\", min_threshold = 0.01)\n",
+    "rules_ap"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Decision Tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn import tree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/lung cancer.csv')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.isnull().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "le_GENDER = LabelEncoder()\n",
+    "le_LUNG_CANCER = LabelEncoder()\n",
+    "\n",
+    "df['Gender'] = le_GENDER.fit_transform(df['GENDER'])\n",
+    "df['LungCancer'] = le_LUNG_CANCER.fit_transform(df['LUNG_CANCER'])\n",
+    "\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop(['GENDER','LUNG_CANCER'], axis = 1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = df.drop('LungCancer', axis = 1)\n",
+    "target = df['LungCancer']\n",
+    "inputs.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import tree\n",
+    "model = tree.DecisionTreeClassifier()\n",
+    "model.fit(inputs, target)\n",
+    "model.score(inputs, target)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(model.predict([[63,1,2,2,1,2,1,1,2,1,2,1,1,2,2]]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tree.plot_tree(model) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#fig, axes = plt.subplots(nrows = 1, ncols = 1, figsize = (2,2), dpi = 300)\n",
+    "#tree.plot_tree(model,\n",
+    " #             feature_names = 'AGE''SMOKING''YELLOW_FINGERS''ANXIETY''PEER_PRESSURE''CHRONIC DISEASE''FATIGUE''ALLERGY''WHEEZING''ALCOHOL CONSUMING''COUGHING''SHORTNESS OF BREATH''SWALLOWING DIFFICULTY''CHEST PAIN''GENDER',\n",
+    "  #            class_names = ['true', 'false'],\n",
+    "   #               filled =True, rounded=True,\n",
+    "    #          )\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SVM bank loan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn\n",
+    "import sklearn\n",
+    "from sklearn import model_selection\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import svm\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn import metrics\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.metrics import roc_curve, auc "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bl = pd.read_csv(\"C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/bankloan.csv\")\n",
+    "bl = pd.DataFrame(bl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bl_data = bl.drop(['address','ed','debtinc','employ'],1)\n",
+    "bl_data.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = bl_data.drop('default', axis = 1)\n",
+    "y = bl_data.default\n",
+    "x.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.4, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# building an SVM having penalty 10 and gamma auto optimized\n",
+    "svct = SVC(kernel = 'linear',\n",
+    "               C = 10, gamma = 'auto', \n",
+    "               probability = True).fit(x_train, y_train)\n",
+    "\n",
+    "print(svct)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = svct.predict(x_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cnf = confusion_matrix(y_test, y_pred)\n",
+    "cnf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# ROC Curve with AUC Plotting\n",
+    "# finding probabilities for 0 and 1 classses\n",
+    "preds1 = svct.predict_proba(x_test)[:,1]\n",
+    "print(preds1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import roc_curve, auc \n",
+    "fpr1, tpr1, thresholds1 = metrics.roc_curve(y_test, preds1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fpr1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tpr1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thresholds1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = pd.DataFrame(dict(fpr = fpr1, tpr = tpr1))\n",
+    "auc = metrics.auc(fpr1, tpr1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plotting\n",
+    "plt.figure()\n",
+    "plt.plot(fpr1, tpr1, color = 'darkorange', label = 'ROC CURVE(area = %0.2f)' % auc)\n",
+    "plt.plot([0,1], [0,1], color = 'navy')\n",
+    "plt.xlim([0.0, 1.0])\n",
+    "plt.ylim([0.0, 1.05])\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC example')\n",
+    "plt.legend(loc = 'lower right')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SVM Iris"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import svm\n",
+    "import pandas as pd\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/iris_df.csv\")\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = ['X4','X3','X1','X2','Y']\n",
+    "df = df.drop(['X4','X3'],1)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import svm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df.drop(\"Y\", axis = 1)\n",
+    "y = df.Y\n",
+    "\n",
+    "support = svm.SVC()\n",
+    "\n",
+    "\n",
+    "trainx, testx, trainy, testy = train_test_split(x,y, test_size = 0.3)\n",
+    "support.fit(trainx, trainy)\n",
+    "print('Accuracy:/n', support.score(testx, testy))\n",
+    "\n",
+    "pred = support.predict(testx)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear regression single variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn import linear_model\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/house_price.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.xlabel('area')\n",
+    "plt.ylabel('price')\n",
+    "plt.scatter(df.area,df.price,color = 'blue')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg = linear_model.LinearRegression()\n",
+    "reg.fit(df[['area']], df.price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# prediction\n",
+    "reg.predict([[3100]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.xlabel('area')\n",
+    "plt.ylabel('price')\n",
+    "plt.scatter(df.area,df.price,color='green')\n",
+    "plt.plot(df.area,reg.predict(df[['area']]), color = 'red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Linear Regression Multiple Variable\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pandas numpy\n",
+    "from sklearn import linear_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/house_pricemodel.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.bedrooms.median()\n",
+    "df.bedrooms = df.bedrooms.fillna(df.bedrooms.median())\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg = linear_model.LinearRegression()\n",
+    "reg.fit(df.drop('price', axis = 1), df.price)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reg.predict([[3200,3,10]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## SImple Logistic Regression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pandas, matplotlib\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"C:/Users/Balakrishnan/Desktop/6th sem/Data Mining/Datasets/insurance_age.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(df.age,df.bought_insurance, marker ='*', color = 'purple')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train, x_test, y_train, y_test = train_test_split(df[['age']], df.bought_insurance, train_size=0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = LogisticRegression()\n",
+    "model.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predicted = model.predict(x_test)\n",
+    "model.predict_proba(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.score(x_test, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_predicted"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.predict([[45]])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Gradient descent simple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gradient_descent(x,y):\n",
+    "    m_curr = b_curr = 0\n",
+    "    iterations = 10000\n",
+    "    n = len(x)     # length of x\n",
+    "    learning_rate = 0.001\n",
+    "    #learning rate = .00001\n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    for i in range(iterations):\n",
+    "        y_predicted = m_curr * x + b_curr\n",
+    "        cost = (1/n) * sum([val**2 for val in (y-y_predicted)])\n",
+    "        md = -(2/n)*sum(x*(y-y_predicted))     # m derivative\n",
+    "        bd = -(2/n)*sum(y-y_predicted)         # b derivative\n",
+    "        m_curr = m_curr - learning_rate * md\n",
+    "        b_curr = b_curr - learning_rate * bd\n",
+    "        #print (\"m {}, b {}, iteration {}\".format(m_curr,b_curr, i))\n",
+    "        print (\"m {}, b {}, cost {} iteration {}\".format(m_curr,b_curr,cost, i))    # each iteration to print the values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.array([1,2,3,4,5])\n",
+    "y = np.array([5,7,9,11,13])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gradient_descent(x,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "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": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}