names classifier version 1 .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "rownames=pd.read_excel(\"tunisian names.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51, 2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rownames.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames.gender.replace({'female':1 , 'male':0},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames['last_letter']=  rownames.name.str[-1]\n",
    "rownames['second_last_letter']=  rownames.name.str[-2]\n",
    "rownames['third_last_letter']=  rownames['name'].apply(lambda x: x[-3] if len(x) > 2 else \"-\");\n",
    "#if the name shorter than 2 letters is will be replaced with - eventually with 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rownames['A']=rownames['last_letter'].apply(lambda x: 1 if 'a' in x  else 0) \n",
    "#add feature whether the name ends with a or not.\n",
    "#if it ends with a it s more probably to be a girl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=rownames[['A']] #the features\n",
    "y=rownames[['gender']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n",
      "C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import seaborn as sns       \n",
    "from sklearn.metrics import accuracy_score, precision_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "#apply random forest\n",
    "model = RandomForestClassifier()\n",
    "model.fit(X_train,y_train)\n",
    "predictions = model.predict(X_test)\n",
    "#confusion matrix\n",
    "sns.heatmap(confusion_matrix(y_test, predictions), annot=True, cmap=\"YlGn\")\n",
    "plt.title(' RandomForestClassifier ')\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the accuracy score for Random Forest Classifier is : 0.5625\n",
      "the precision score is : 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1437: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "print(\"the accuracy score for Random Forest Classifier is :\",(accuracy_score(y_test, predictions)))\n",
    "print(\"the precision score is :\",(precision_score(y_test, predictions)))"
   ]
  },
  {
   "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",
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