Added a LOAN prediction project using python with jupyter based on Data Analysis

Author: Tomyzon1728

projects/LOAN/Project_2 LOAN.ipynb

@@ -0,0 +1,90 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''\n",
+    "LOAN DATASET\n",
+    "'''\n",
+    "\n",
+    "# required libraries\n",
+    "import pandas as pd\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "\n",
+    "\n",
+    "# read the dataset\n",
+    "data = pd.read_csv('train_ctrUa4K.csv')\n",
+    "print(data.head())\n",
+    "\n",
+    "print('\\n\\nColumn Names\\n\\n')\n",
+    "print(data.columns)\n",
+    "\n",
+    "#label encode the target variable\n",
+    "encode = LabelEncoder()\n",
+    "data.Loan_Status = encode.fit_transform(data.Loan_Status)\n",
+    "\n",
+    "# drop the null values\n",
+    "data.dropna(how='any',inplace=True)\n",
+    "\n",
+    "\n",
+    "# train-test-split   \n",
+    "train , test = train_test_split(data,test_size=0.2,random_state=0)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# seperate the target and independent variable\n",
+    "train_x = train.drop(columns=['Loan_ID','Loan_Status'],axis=1)\n",
+    "train_y = train['Loan_Status']\n",
+    "\n",
+    "test_x = test.drop(columns=['Loan_ID','Loan_Status'],axis=1)\n",
+    "test_y = test['Loan_Status']\n",
+    "\n",
+    "# encode the data\n",
+    "train_x = pd.get_dummies(train_x)\n",
+    "test_x  = pd.get_dummies(test_x)\n",
+    "\n",
+    "print('shape of training data : ',train_x.shape)\n",
+    "print('shape of testing data : ',test_x.shape)\n",
+    "\n",
+    "# create the object of the model\n",
+    "model = LogisticRegression()\n",
+    "\n",
+    "model.fit(train_x,train_y)\n",
+    "\n",
+    "predict = model.predict(test_x)\n",
+    "\n",
+    "print('Predicted Values on Test Data',predict)\n",
+    "\n",
+    "print('\\n\\nAccuracy Score on test data : \\n\\n')\n",
+    "print(accuracy_score(test_y,predict))\n"
+   ]
+  }
+ ],
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+   "language": "python",
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