Added GNB, KNeighbour, and SVC ML implementations

nlp_gnb.ipynb

@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 128,
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:10.395299Z",
-     "start_time": "2023-05-18T02:41:10.386829Z"
+     "end_time": "2023-05-18T12:11:19.523639Z",
+     "start_time": "2023-05-18T12:11:19.519384Z"
     }
    },
    "outputs": [],
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 129,
    "outputs": [],
    "source": [
     "data = vectorizer.fit_transform(edn.loads(open(\"sample_data.txt\").read()))\n",
@@ -30,14 +30,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:10.409258Z",
-     "start_time": "2023-05-18T02:41:10.397755Z"
+     "end_time": "2023-05-18T12:11:19.550492Z",
+     "start_time": "2023-05-18T12:11:19.526161Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 130,
    "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)"
@@ -45,14 +45,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:10.415818Z",
-     "start_time": "2023-05-18T02:41:10.412071Z"
+     "end_time": "2023-05-18T12:11:19.558891Z",
+     "start_time": "2023-05-18T12:11:19.553282Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 131,
    "outputs": [
     {
      "name": "stdout",
@@ -71,8 +71,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:10.428856Z",
-     "start_time": "2023-05-18T02:41:10.417634Z"
+     "end_time": "2023-05-18T12:11:19.588742Z",
+     "start_time": "2023-05-18T12:11:19.561239Z"
     }
    }
   }

nlp_kn.ipynb

@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 80,
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:18.336164Z",
-     "start_time": "2023-05-18T02:41:18.331276Z"
+     "end_time": "2023-05-18T12:11:21.691592Z",
+     "start_time": "2023-05-18T12:11:21.688239Z"
     }
    },
    "outputs": [],
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 81,
    "outputs": [],
    "source": [
     "data = vectorizer.fit_transform(edn.loads(open(\"sample_data.txt\").read()))\n",
@@ -30,14 +30,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:18.353544Z",
-     "start_time": "2023-05-18T02:41:18.336411Z"
+     "end_time": "2023-05-18T12:11:21.723895Z",
+     "start_time": "2023-05-18T12:11:21.697871Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 82,
    "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)"
@@ -45,14 +45,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:18.359671Z",
-     "start_time": "2023-05-18T02:41:18.356890Z"
+     "end_time": "2023-05-18T12:11:21.736415Z",
+     "start_time": "2023-05-18T12:11:21.734204Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 83,
    "outputs": [
     {
      "name": "stdout",
@@ -71,8 +71,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:18.370524Z",
-     "start_time": "2023-05-18T02:41:18.362461Z"
+     "end_time": "2023-05-18T12:11:21.759108Z",
+     "start_time": "2023-05-18T12:11:21.740374Z"
     }
    }
   }

nlp_svc.ipynb

@@ -2,12 +2,12 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 461,
+   "execution_count": 469,
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:46.523940Z",
-     "start_time": "2023-05-18T02:41:46.517479Z"
+     "end_time": "2023-05-18T12:11:14.027094Z",
+     "start_time": "2023-05-18T12:11:14.025377Z"
     }
    },
    "outputs": [],
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 462,
+   "execution_count": 470,
    "outputs": [],
    "source": [
     "data = vectorizer.fit_transform(edn.loads(open(\"sample_data.txt\").read()))\n",
@@ -30,14 +30,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:46.541875Z",
-     "start_time": "2023-05-18T02:41:46.524547Z"
+     "end_time": "2023-05-18T12:11:14.068427Z",
+     "start_time": "2023-05-18T12:11:14.028784Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 463,
+   "execution_count": 471,
    "outputs": [],
    "source": [
     "X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)"
@@ -45,14 +45,14 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:46.550473Z",
-     "start_time": "2023-05-18T02:41:46.544756Z"
+     "end_time": "2023-05-18T12:11:14.083705Z",
+     "start_time": "2023-05-18T12:11:14.077326Z"
     }
    }
   },
   {
    "cell_type": "code",
-   "execution_count": 464,
+   "execution_count": 472,
    "outputs": [
     {
      "name": "stdout",
@@ -71,8 +71,8 @@
    "metadata": {
     "collapsed": false,
     "ExecuteTime": {
-     "end_time": "2023-05-18T02:41:46.559888Z",
-     "start_time": "2023-05-18T02:41:46.551307Z"
+     "end_time": "2023-05-18T12:11:14.114268Z",
+     "start_time": "2023-05-18T12:11:14.092526Z"
     }
    }
   }