Accuracy prediction of Machine learning Algorithms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>1.127087</td>\n",
       "      <td>1.332534</td>\n",
       "      <td>1.328893</td>\n",
       "      <td>1.305142</td>\n",
       "      <td>0.804407</td>\n",
       "      <td>1.017605</td>\n",
       "      <td>1.017485</td>\n",
       "      <td>0.991847</td>\n",
       "      <td>0.575397</td>\n",
       "      <td>0.587244</td>\n",
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       "      <td>0.090627</td>\n",
       "      <td>0.084655</td>\n",
       "      <td>0.095734</td>\n",
       "      <td>0.067779</td>\n",
       "      <td>0.081946</td>\n",
       "      <td>0.058241</td>\n",
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       "      <td>1.337421</td>\n",
       "      <td>1.333921</td>\n",
       "      <td>1.310700</td>\n",
       "      <td>0.802088</td>\n",
       "      <td>1.015296</td>\n",
       "      <td>1.015148</td>\n",
       "      <td>0.989509</td>\n",
       "      <td>0.575327</td>\n",
       "      <td>0.586249</td>\n",
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       "      <td>0.396193</td>\n",
       "      <td>0.090195</td>\n",
       "      <td>0.086384</td>\n",
       "      <td>0.097342</td>\n",
       "      <td>0.069010</td>\n",
       "      <td>0.082106</td>\n",
       "      <td>0.058203</td>\n",
       "      <td>0.063062</td>\n",
       "      <td>0.048842</td>\n",
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       "      <td>1.342521</td>\n",
       "      <td>1.339171</td>\n",
       "      <td>1.316510</td>\n",
       "      <td>0.799617</td>\n",
       "      <td>1.012833</td>\n",
       "      <td>1.012659</td>\n",
       "      <td>0.987012</td>\n",
       "      <td>0.575228</td>\n",
       "      <td>0.585174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448206</td>\n",
       "      <td>0.396145</td>\n",
       "      <td>0.089767</td>\n",
       "      <td>0.088228</td>\n",
       "      <td>0.099043</td>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.082279</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.063040</td>\n",
       "      <td>0.048781</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.141961</td>\n",
       "      <td>1.347770</td>\n",
       "      <td>1.344579</td>\n",
       "      <td>1.322506</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>1.010229</td>\n",
       "      <td>1.010033</td>\n",
       "      <td>0.984370</td>\n",
       "      <td>0.575094</td>\n",
       "      <td>0.584023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448160</td>\n",
       "      <td>0.396093</td>\n",
       "      <td>0.089354</td>\n",
       "      <td>0.090171</td>\n",
       "      <td>0.100820</td>\n",
       "      <td>0.071672</td>\n",
       "      <td>0.082464</td>\n",
       "      <td>0.058132</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.048717</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.147230</td>\n",
       "      <td>1.353103</td>\n",
       "      <td>1.350078</td>\n",
       "      <td>1.328615</td>\n",
       "      <td>0.794281</td>\n",
       "      <td>1.007500</td>\n",
       "      <td>1.007287</td>\n",
       "      <td>0.981602</td>\n",
       "      <td>0.574925</td>\n",
       "      <td>0.582801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448109</td>\n",
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       "      <td>0.088963</td>\n",
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       "      <td>0.073079</td>\n",
       "      <td>0.082663</td>\n",
       "      <td>0.058101</td>\n",
       "      <td>0.062985</td>\n",
       "      <td>0.048651</td>\n",
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       "    L_FCC_x   L_FM1_x   L_FM2_x   L_FM5_x   R_FCC_x   R_FM1_x   R_FM2_x  \\\n",
       "0  1.127087  1.332534  1.328893  1.305142  0.804407  1.017605  1.017485   \n",
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       "\n",
       "    R_FM5_x   L_FCC_y   L_FM1_y  ...   R_FM2_y   R_FM5_y   L_FCC_z   L_FM1_z  \\\n",
       "0  0.991847  0.575397  0.587244  ...  0.448289  0.396237  0.090627  0.084655   \n",
       "1  0.989509  0.575327  0.586249  ...  0.448250  0.396193  0.090195  0.086384   \n",
       "2  0.987012  0.575228  0.585174  ...  0.448206  0.396145  0.089767  0.088228   \n",
       "3  0.984370  0.575094  0.584023  ...  0.448160  0.396093  0.089354  0.090171   \n",
       "4  0.981602  0.574925  0.582801  ...  0.448109  0.396038  0.088963  0.092198   \n",
       "\n",
       "    L_FM2_z   L_FM5_z   R_FCC_z   R_FM1_z   R_FM2_z   R_FM5_z  \n",
       "0  0.095734  0.067779  0.081946  0.058241  0.063082  0.048901  \n",
       "1  0.097342  0.069010  0.082106  0.058203  0.063062  0.048842  \n",
       "2  0.099043  0.070312  0.082279  0.058166  0.063040  0.048781  \n",
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     "execution_count": 2,
     "metadata": {},
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   "source": [
    "datasix1 = pd.read_csv(r'C:\\Users\\hp\\Desktop\\Major project (Final year)_24\\DATA\\Human Gait Phase Dataset [Master Data]\\data\\GP1_0.6_marker.csv')\n",
    "dataseven2 = pd.read_csv(r'C:\\Users\\hp\\Desktop\\Major project (Final year)_24\\DATA\\Human Gait Phase Dataset [Master Data]\\data\\GP1_0.7_marker.csv')\n",
    "datasix1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1.333921</td>\n",
       "      <td>1.310700</td>\n",
       "      <td>0.802088</td>\n",
       "      <td>1.015296</td>\n",
       "      <td>1.015148</td>\n",
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       "      <td>0.396193</td>\n",
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       "      <td>0.082106</td>\n",
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       "      <th>2</th>\n",
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       "      <td>1.342521</td>\n",
       "      <td>1.339171</td>\n",
       "      <td>1.316510</td>\n",
       "      <td>0.799617</td>\n",
       "      <td>1.012833</td>\n",
       "      <td>1.012659</td>\n",
       "      <td>0.987012</td>\n",
       "      <td>0.575228</td>\n",
       "      <td>0.585174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396145</td>\n",
       "      <td>0.089767</td>\n",
       "      <td>0.088228</td>\n",
       "      <td>0.099043</td>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.082279</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.063040</td>\n",
       "      <td>0.048781</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.141961</td>\n",
       "      <td>1.347770</td>\n",
       "      <td>1.344579</td>\n",
       "      <td>1.322506</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>1.010229</td>\n",
       "      <td>1.010033</td>\n",
       "      <td>0.984370</td>\n",
       "      <td>0.575094</td>\n",
       "      <td>0.584023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396093</td>\n",
       "      <td>0.089354</td>\n",
       "      <td>0.090171</td>\n",
       "      <td>0.100820</td>\n",
       "      <td>0.071672</td>\n",
       "      <td>0.082464</td>\n",
       "      <td>0.058132</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.048717</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>1.147230</td>\n",
       "      <td>1.353103</td>\n",
       "      <td>1.350078</td>\n",
       "      <td>1.328615</td>\n",
       "      <td>0.794281</td>\n",
       "      <td>1.007500</td>\n",
       "      <td>1.007287</td>\n",
       "      <td>0.981602</td>\n",
       "      <td>0.574925</td>\n",
       "      <td>0.582801</td>\n",
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       "      <td>0.396038</td>\n",
       "      <td>0.088963</td>\n",
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       "      <td>0.102656</td>\n",
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       "      <td>0.058101</td>\n",
       "      <td>0.062985</td>\n",
       "      <td>0.048651</td>\n",
       "      <td>0.0</td>\n",
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       "    L_FCC_x   L_FM1_x   L_FM2_x   L_FM5_x   R_FCC_x   R_FM1_x   R_FM2_x  \\\n",
       "0  1.127087  1.332534  1.328893  1.305142  0.804407  1.017605  1.017485   \n",
       "1  1.131831  1.337421  1.333921  1.310700  0.802088  1.015296  1.015148   \n",
       "2  1.136807  1.342521  1.339171  1.316510  0.799617  1.012833  1.012659   \n",
       "3  1.141961  1.347770  1.344579  1.322506  0.797009  1.010229  1.010033   \n",
       "4  1.147230  1.353103  1.350078  1.328615  0.794281  1.007500  1.007287   \n",
       "\n",
       "    R_FM5_x   L_FCC_y   L_FM1_y  ...   R_FM5_y   L_FCC_z   L_FM1_z   L_FM2_z  \\\n",
       "0  0.991847  0.575397  0.587244  ...  0.396237  0.090627  0.084655  0.095734   \n",
       "1  0.989509  0.575327  0.586249  ...  0.396193  0.090195  0.086384  0.097342   \n",
       "2  0.987012  0.575228  0.585174  ...  0.396145  0.089767  0.088228  0.099043   \n",
       "3  0.984370  0.575094  0.584023  ...  0.396093  0.089354  0.090171  0.100820   \n",
       "4  0.981602  0.574925  0.582801  ...  0.396038  0.088963  0.092198  0.102656   \n",
       "\n",
       "    L_FM5_z   R_FCC_z   R_FM1_z   R_FM2_z   R_FM5_z  secim  \n",
       "0  0.067779  0.081946  0.058241  0.063082  0.048901    0.0  \n",
       "1  0.069010  0.082106  0.058203  0.063062  0.048842    0.0  \n",
       "2  0.070312  0.082279  0.058166  0.063040  0.048781    0.0  \n",
       "3  0.071672  0.082464  0.058132  0.063014  0.048717    0.0  \n",
       "4  0.073079  0.082663  0.058101  0.062985  0.048651    0.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.DataFrame({'secim':np.zeros(12000)})\n",
    "data2 = pd.DataFrame({'secim':np.ones(12000)})\n",
    "\n",
    "data2 = pd.concat([dataseven2,data2],axis=1)\n",
    "data1 = pd.concat([datasix1,data1],axis=1)\n",
    "\n",
    "data = data1.append(data2,ignore_index=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th>R_FM5_x</th>\n",
       "      <th>L_FCC_y</th>\n",
       "      <th>L_FM1_y</th>\n",
       "      <th>...</th>\n",
       "      <th>R_FM2_y</th>\n",
       "      <th>R_FM5_y</th>\n",
       "      <th>L_FCC_z</th>\n",
       "      <th>L_FM1_z</th>\n",
       "      <th>L_FM2_z</th>\n",
       "      <th>L_FM5_z</th>\n",
       "      <th>R_FCC_z</th>\n",
       "      <th>R_FM1_z</th>\n",
       "      <th>R_FM2_z</th>\n",
       "      <th>R_FM5_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.127087</td>\n",
       "      <td>1.332534</td>\n",
       "      <td>1.328893</td>\n",
       "      <td>1.305142</td>\n",
       "      <td>0.804407</td>\n",
       "      <td>1.017605</td>\n",
       "      <td>1.017485</td>\n",
       "      <td>0.991847</td>\n",
       "      <td>0.575397</td>\n",
       "      <td>0.587244</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448289</td>\n",
       "      <td>0.396237</td>\n",
       "      <td>0.090627</td>\n",
       "      <td>0.084655</td>\n",
       "      <td>0.095734</td>\n",
       "      <td>0.067779</td>\n",
       "      <td>0.081946</td>\n",
       "      <td>0.058241</td>\n",
       "      <td>0.063082</td>\n",
       "      <td>0.048901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.131831</td>\n",
       "      <td>1.337421</td>\n",
       "      <td>1.333921</td>\n",
       "      <td>1.310700</td>\n",
       "      <td>0.802088</td>\n",
       "      <td>1.015296</td>\n",
       "      <td>1.015148</td>\n",
       "      <td>0.989509</td>\n",
       "      <td>0.575327</td>\n",
       "      <td>0.586249</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448250</td>\n",
       "      <td>0.396193</td>\n",
       "      <td>0.090195</td>\n",
       "      <td>0.086384</td>\n",
       "      <td>0.097342</td>\n",
       "      <td>0.069010</td>\n",
       "      <td>0.082106</td>\n",
       "      <td>0.058203</td>\n",
       "      <td>0.063062</td>\n",
       "      <td>0.048842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.136807</td>\n",
       "      <td>1.342521</td>\n",
       "      <td>1.339171</td>\n",
       "      <td>1.316510</td>\n",
       "      <td>0.799617</td>\n",
       "      <td>1.012833</td>\n",
       "      <td>1.012659</td>\n",
       "      <td>0.987012</td>\n",
       "      <td>0.575228</td>\n",
       "      <td>0.585174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448206</td>\n",
       "      <td>0.396145</td>\n",
       "      <td>0.089767</td>\n",
       "      <td>0.088228</td>\n",
       "      <td>0.099043</td>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.082279</td>\n",
       "      <td>0.058166</td>\n",
       "      <td>0.063040</td>\n",
       "      <td>0.048781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.141961</td>\n",
       "      <td>1.347770</td>\n",
       "      <td>1.344579</td>\n",
       "      <td>1.322506</td>\n",
       "      <td>0.797009</td>\n",
       "      <td>1.010229</td>\n",
       "      <td>1.010033</td>\n",
       "      <td>0.984370</td>\n",
       "      <td>0.575094</td>\n",
       "      <td>0.584023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448160</td>\n",
       "      <td>0.396093</td>\n",
       "      <td>0.089354</td>\n",
       "      <td>0.090171</td>\n",
       "      <td>0.100820</td>\n",
       "      <td>0.071672</td>\n",
       "      <td>0.082464</td>\n",
       "      <td>0.058132</td>\n",
       "      <td>0.063014</td>\n",
       "      <td>0.048717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.147230</td>\n",
       "      <td>1.353103</td>\n",
       "      <td>1.350078</td>\n",
       "      <td>1.328615</td>\n",
       "      <td>0.794281</td>\n",
       "      <td>1.007500</td>\n",
       "      <td>1.007287</td>\n",
       "      <td>0.981602</td>\n",
       "      <td>0.574925</td>\n",
       "      <td>0.582801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.448109</td>\n",
       "      <td>0.396038</td>\n",
       "      <td>0.088963</td>\n",
       "      <td>0.092198</td>\n",
       "      <td>0.102656</td>\n",
       "      <td>0.073079</td>\n",
       "      <td>0.082663</td>\n",
       "      <td>0.058101</td>\n",
       "      <td>0.062985</td>\n",
       "      <td>0.048651</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    L_FCC_x   L_FM1_x   L_FM2_x   L_FM5_x   R_FCC_x   R_FM1_x   R_FM2_x  \\\n",
       "0  1.127087  1.332534  1.328893  1.305142  0.804407  1.017605  1.017485   \n",
       "1  1.131831  1.337421  1.333921  1.310700  0.802088  1.015296  1.015148   \n",
       "2  1.136807  1.342521  1.339171  1.316510  0.799617  1.012833  1.012659   \n",
       "3  1.141961  1.347770  1.344579  1.322506  0.797009  1.010229  1.010033   \n",
       "4  1.147230  1.353103  1.350078  1.328615  0.794281  1.007500  1.007287   \n",
       "\n",
       "    R_FM5_x   L_FCC_y   L_FM1_y  ...   R_FM2_y   R_FM5_y   L_FCC_z   L_FM1_z  \\\n",
       "0  0.991847  0.575397  0.587244  ...  0.448289  0.396237  0.090627  0.084655   \n",
       "1  0.989509  0.575327  0.586249  ...  0.448250  0.396193  0.090195  0.086384   \n",
       "2  0.987012  0.575228  0.585174  ...  0.448206  0.396145  0.089767  0.088228   \n",
       "3  0.984370  0.575094  0.584023  ...  0.448160  0.396093  0.089354  0.090171   \n",
       "4  0.981602  0.574925  0.582801  ...  0.448109  0.396038  0.088963  0.092198   \n",
       "\n",
       "    L_FM2_z   L_FM5_z   R_FCC_z   R_FM1_z   R_FM2_z   R_FM5_z  \n",
       "0  0.095734  0.067779  0.081946  0.058241  0.063082  0.048901  \n",
       "1  0.097342  0.069010  0.082106  0.058203  0.063062  0.048842  \n",
       "2  0.099043  0.070312  0.082279  0.058166  0.063040  0.048781  \n",
       "3  0.100820  0.071672  0.082464  0.058132  0.063014  0.048717  \n",
       "4  0.102656  0.073079  0.082663  0.058101  0.062985  0.048651  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.drop([\"secim\"],axis=1)\n",
    "y = data.secim.values\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of svm is : 99 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC(random_state = 1)\n",
    "svm.fit(x_train,y_train)\n",
    "print(\"Accuracy of svm is :\",int(svm.score(x_test,y_test)*100),\"%\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Bayes in test data is : 93 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "nb = GaussianNB()\n",
    "nb.fit(x_train,y_train)\n",
    "print('Accuracy of Bayes in test data is :', int(nb.score(x_test,y_test)*100),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of decision tree in test data is: 99 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(x_train,y_train)\n",
    "print('Accuracy of decision tree in test data is:',int(dt.score(x_test,y_test)*100),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Random Forest on test data is :  100 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100,random_state=1)\n",
    "rf.fit(x_train,y_train)\n",
    "print('Accuracy of Random Forest on test data is : ',int(rf.score(x_test,y_test)*100),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test accuracy for Log Regressin is  98.68055555555556 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(x_train,y_train)\n",
    "print(\"test accuracy for Log Regressin is  {}\".format(lr.score(x_test,y_test)*100),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=3 nn Accuracy:100 %\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 3) #n_neighbors = k\n",
    "knn.fit(x_train,y_train)\n",
    "prediction = knn.predict(x_test)\n",
    "\n",
    "print(\"k={} nn Accuracy:{}\".format(3,int(knn.score(x_test,y_test))*100),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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