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+# Logistic Regression from scratch
+The code is implemented in Python
+## Methodology
+Below are the methods used in writing Logistic Regression from scratch
+
+1. Initialize a class Logistic Regression
+Logistic Regression was used in the biological sciences in early twentieth century. It was then used in many social science applications. It is used when the dependent variable(target) is categorical.
+For example,
+**To predict whether an email is spam (1) or (0)
+**Whether the tumor is malignant (1) or not (0)
+
+It was initialized with attribute learning rate (lr), number of iteration(n_iters). 
+The amount that the weights are updated during training is referred to as the step size or the “learning rate.”
+The bias value allows the activation function to be shifted to the left or right, to better fit the data.
+
+2. Sigmoid Function
+The formula for linear function which produces continuous variables is;
+        f(w,b) = wx + b
+where w is the weight and b is bias and x is the data point. Since linear regression is used when the dependent variable(target) is categorical. Sigmoid function tends to convert the linear function to probalilities i.e 0 and 1.
+Sigmoid function is 1 / (1 + e^-1)
+
+3. Gradient Descent
+Gradient Descent is used to iteratively update the weight, along with the learning rate to know how far the direction will go.
+
diff --git a/Olaniyan Oluwasegun Emmanuel/logistic_regression.ipynb b/Olaniyan Oluwasegun Emmanuel/logistic_regression.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# define class Logistic Regression\n",
+    "class LogisticRegression:\n",
+    "    def __init__(self, lr=0.001, n_iters=1000):\n",
+    "        self.lr = lr\n",
+    "        self.n_iters = n_iters\n",
+    "        self.weights = None\n",
+    "        self.bias = None\n",
+    "    \n",
+    "    def fit(self, X, y):\n",
+    "        \"\"\"\n",
+    "        Trains the model from the training data\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x: array-like, shape = [n_samples, n_features]\n",
+    "            Training samples\n",
+    "        y: array-like, shape = [n_samples, n_target_values]\n",
+    "            Target classes\n",
+    "        Returns\n",
+    "        -------\n",
+    "        self: An instance of self\n",
+    "        \"\"\"\n",
+    "\n",
+    "        # initialize parameters\n",
+    "        n_samples, n_features = X.shape\n",
+    "        self.weights = np.zeros(n_features)\n",
+    "        self.bias = 0\n",
+    "     \n",
+    "    # sigmoid function to convert the linear function to probabilities\n",
+    "    def _sigmoid(self, x):\n",
+    "        return 1 / (1 + np.exp(-x))   \n",
+    "    \n",
+    "        # gradient descent to update the weight and bias\n",
+    "        for __ in range(self.n_iters):\n",
+    "            linearModel = np.dot(X, self.weights) + self.bias\n",
+    "            y_pred = self._sigmoid(linearModel)\n",
+    "        # using update rules\n",
+    "            dw = (1 / n_samples) * np.dot(X.T, (y_pred - y))\n",
+    "            db = (1 / n_samples) * np.sum(y_pred - y)\n",
+    "            \n",
+    "            self.weights -= self.lr * dw\n",
+    "            self.bias -= self.lr * db\n",
+    "    \n",
+    "    def predict(self, X):\n",
+    "        \"\"\" Predicts the class labels\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x: array-like, shape = [n_samples, n_features]\n",
+    "            Test samples\n",
+    "        Returns\n",
+    "        -------\n",
+    "        predicted class labels\n",
+    "        \"\"\"\n",
+    "        linearModel = np.dot(X, self.weights) + self.bias\n",
+    "        y_pred = self._sigmoid(linearModel)\n",
+    "        y_pred_cls = [1 if i > 0.5 else 0 for i in y_pred]\n",
+    "        return y_pred_cls\n",
+    "   "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy is 0.93\n"
+     ]
+    }
+   ],
+   "source": [
+    "# using breast cancer to test the model\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import load_breast_cancer\n",
+    "from logistic_regression import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "df = load_breast_cancer()\n",
+    "X,y = df.data,df.target\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=1234)\n",
+    "log = LogisticRegression(lr=0.0001,n_iters=1000)\n",
+    "log.fit(X_train,y_train)\n",
+    "pred = log.predict(X_test)\n",
+    "\n",
+    "print('Accuracy is %.2f'%(accuracy_score(pred,y_test)))"
+   ]
+  },
+  {
+   "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": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}