From a1025a9c2674eb58364f697495e28a8f41422b3a Mon Sep 17 00:00:00 2001
From: YashSachan2 <yash.sachan.ece22@itbhu.ac.in>
Date: Tue, 2 Jan 2024 23:08:17 +0530
Subject: [PATCH] Bearing Classification

---
 .../Dataset/Faulty-bearing.csv                | 1000 ++++++++++
 .../Dataset/Healthy-bearing.csv               | 1000 ++++++++++
 Bearing Classification/Images/EDA1.png        |  Bin 0 -> 8603 bytes
 Bearing Classification/Images/EDA2.png        |  Bin 0 -> 17536 bytes
 Bearing Classification/Images/EDA3.png        |  Bin 0 -> 14670 bytes
 .../Images/Input_Dataset.png                  |  Bin 0 -> 32758 bytes
 Bearing Classification/Images/Metrics.png     |  Bin 0 -> 18301 bytes
 .../Model/Faulty-bearing.csv                  | 1000 ++++++++++
 .../Model/Healthy-bearing.csv                 | 1000 ++++++++++
 .../Model/bearing_classification.ipynb        | 1649 +++++++++++++++++
 Bearing Classification/README.md              |   68 +
 11 files changed, 5717 insertions(+)
 create mode 100644 Bearing Classification/Dataset/Faulty-bearing.csv
 create mode 100644 Bearing Classification/Dataset/Healthy-bearing.csv
 create mode 100644 Bearing Classification/Images/EDA1.png
 create mode 100644 Bearing Classification/Images/EDA2.png
 create mode 100644 Bearing Classification/Images/EDA3.png
 create mode 100644 Bearing Classification/Images/Input_Dataset.png
 create mode 100644 Bearing Classification/Images/Metrics.png
 create mode 100644 Bearing Classification/Model/Faulty-bearing.csv
 create mode 100644 Bearing Classification/Model/Healthy-bearing.csv
 create mode 100644 Bearing Classification/Model/bearing_classification.ipynb
 create mode 100644 Bearing Classification/README.md

diff --git a/Bearing Classification/Dataset/Faulty-bearing.csv b/Bearing Classification/Dataset/Faulty-bearing.csv
new file mode 100644
index 000000000..2d643d1e4
--- /dev/null
+++ b/Bearing Classification/Dataset/Faulty-bearing.csv	
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diff --git a/Bearing Classification/Dataset/Healthy-bearing.csv b/Bearing Classification/Dataset/Healthy-bearing.csv
new file mode 100644
index 000000000..bc29aabf9
--- /dev/null
+++ b/Bearing Classification/Dataset/Healthy-bearing.csv	
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diff --git a/Bearing Classification/Model/Faulty-bearing.csv b/Bearing Classification/Model/Faulty-bearing.csv
new file mode 100644
index 000000000..2d643d1e4
--- /dev/null
+++ b/Bearing Classification/Model/Faulty-bearing.csv	
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diff --git a/Bearing Classification/Model/Healthy-bearing.csv b/Bearing Classification/Model/Healthy-bearing.csv
new file mode 100644
index 000000000..bc29aabf9
--- /dev/null
+++ b/Bearing Classification/Model/Healthy-bearing.csv	
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diff --git a/Bearing Classification/Model/bearing_classification.ipynb b/Bearing Classification/Model/bearing_classification.ipynb
new file mode 100644
index 000000000..70a407d81
--- /dev/null
+++ b/Bearing Classification/Model/bearing_classification.ipynb	
@@ -0,0 +1,1649 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import plotly.express as px\n",
+    "import plotly.graph_objects as go"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=pd.read_csv('Faulty-bearing.csv',delimiter=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Case_No</th>\n",
+       "      <th>Measurements</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>-0,004725455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>-0,106630909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>-0,074169091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0,208947273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>-0,056705455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-0,175458182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0,051774545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0,041501818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>-0,024449091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0,167445455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>-0,165185455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0,022805455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>0,468641818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>0,242230909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0,019723636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>-0,010889091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>-0,125943636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>-0,041501818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>0,267090909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>-0,074169091</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Case_No  Measurements\n",
+       "0         1  -0,004725455\n",
+       "1         2  -0,106630909\n",
+       "2         3  -0,074169091\n",
+       "3         4   0,208947273\n",
+       "4         5  -0,056705455\n",
+       "5         6  -0,175458182\n",
+       "6         7   0,051774545\n",
+       "7         8   0,041501818\n",
+       "8         9  -0,024449091\n",
+       "9        10   0,167445455\n",
+       "10       11  -0,165185455\n",
+       "11       12   0,022805455\n",
+       "12       13   0,468641818\n",
+       "13       14   0,242230909\n",
+       "14       15   0,019723636\n",
+       "15       16  -0,010889091\n",
+       "16       17  -0,125943636\n",
+       "17       18  -0,041501818\n",
+       "18       19   0,267090909\n",
+       "19       20  -0,074169091"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999, 2)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=df1.drop(['Case_No'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2=pd.read_csv('Healthy-bearing.csv',delimiter=';')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Case_No</th>\n",
+       "      <th>Measurement</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0,097796364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0,054856364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0,036981818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0,054445455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0,021161818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>-0,003698182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>-0,010683636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0,02938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>0,104576364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>0,135805455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0,124505455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0,06102</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>-0,003698182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>-0,027736364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>-0,004930909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>0,004930909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>-0,005547273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>-0,013149091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>-0,003492727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>0,048281818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Case_No   Measurement\n",
+       "0         1   0,097796364\n",
+       "1         2   0,054856364\n",
+       "2         3   0,036981818\n",
+       "3         4   0,054445455\n",
+       "4         5   0,021161818\n",
+       "5         6  -0,003698182\n",
+       "6         7  -0,010683636\n",
+       "7         8       0,02938\n",
+       "8         9   0,104576364\n",
+       "9        10   0,135805455\n",
+       "10       11   0,124505455\n",
+       "11       12       0,06102\n",
+       "12       13  -0,003698182\n",
+       "13       14  -0,027736364\n",
+       "14       15  -0,004930909\n",
+       "15       16   0,004930909\n",
+       "16       17  -0,005547273\n",
+       "17       18  -0,013149091\n",
+       "18       19  -0,003492727\n",
+       "19       20   0,048281818"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(999, 2)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<BarContainer object of 2 artists>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(['Healthy','Faulty'],[999,999])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2=df2.drop(['Case_No'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a=[]\n",
+    "a1=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(999):\n",
+    "    i1=df1['Measurements'][i]\n",
+    "    before_comma, after_comma = i1.split(',')\n",
+    "    before_comma = int(before_comma)\n",
+    "    after_comma = int(after_comma)\n",
+    "    a.append(before_comma)\n",
+    "    a1.append(after_comma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1['final']=a1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Measurements</th>\n",
+       "      <th>final</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0,004725455</td>\n",
+       "      <td>4725455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0,106630909</td>\n",
+       "      <td>106630909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0,074169091</td>\n",
+       "      <td>74169091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0,208947273</td>\n",
+       "      <td>208947273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0,056705455</td>\n",
+       "      <td>56705455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>-0,175458182</td>\n",
+       "      <td>175458182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0,051774545</td>\n",
+       "      <td>51774545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0,041501818</td>\n",
+       "      <td>41501818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>-0,024449091</td>\n",
+       "      <td>24449091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0,167445455</td>\n",
+       "      <td>167445455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>-0,165185455</td>\n",
+       "      <td>165185455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0,022805455</td>\n",
+       "      <td>22805455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>0,468641818</td>\n",
+       "      <td>468641818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>0,242230909</td>\n",
+       "      <td>242230909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>0,019723636</td>\n",
+       "      <td>19723636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>-0,010889091</td>\n",
+       "      <td>10889091</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>-0,125943636</td>\n",
+       "      <td>125943636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>-0,041501818</td>\n",
+       "      <td>41501818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0,267090909</td>\n",
+       "      <td>267090909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>-0,074169091</td>\n",
+       "      <td>74169091</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Measurements      final\n",
+       "0   -0,004725455    4725455\n",
+       "1   -0,106630909  106630909\n",
+       "2   -0,074169091   74169091\n",
+       "3    0,208947273  208947273\n",
+       "4   -0,056705455   56705455\n",
+       "5   -0,175458182  175458182\n",
+       "6    0,051774545   51774545\n",
+       "7    0,041501818   41501818\n",
+       "8   -0,024449091   24449091\n",
+       "9    0,167445455  167445455\n",
+       "10  -0,165185455  165185455\n",
+       "11   0,022805455   22805455\n",
+       "12   0,468641818  468641818\n",
+       "13   0,242230909  242230909\n",
+       "14   0,019723636   19723636\n",
+       "15  -0,010889091   10889091\n",
+       "16  -0,125943636  125943636\n",
+       "17  -0,041501818   41501818\n",
+       "18   0,267090909  267090909\n",
+       "19  -0,074169091   74169091"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1['lable']=[0]*999"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1=df1.drop('Measurements',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>final</th>\n",
+       "      <th>lable</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4725455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>106630909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>74169091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>208947273</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>56705455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>175458182</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>51774545</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>41501818</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>24449091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>167445455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>165185455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>22805455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>468641818</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>242230909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>19723636</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10889091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>125943636</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>41501818</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>267090909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>74169091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        final  lable\n",
+       "0     4725455      0\n",
+       "1   106630909      0\n",
+       "2    74169091      0\n",
+       "3   208947273      0\n",
+       "4    56705455      0\n",
+       "5   175458182      0\n",
+       "6    51774545      0\n",
+       "7    41501818      0\n",
+       "8    24449091      0\n",
+       "9   167445455      0\n",
+       "10  165185455      0\n",
+       "11   22805455      0\n",
+       "12  468641818      0\n",
+       "13  242230909      0\n",
+       "14   19723636      0\n",
+       "15   10889091      0\n",
+       "16  125943636      0\n",
+       "17   41501818      0\n",
+       "18  267090909      0\n",
+       "19   74169091      0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a2=[]\n",
+    "a3=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(999):\n",
+    "    i1=df2['Measurement'][i]\n",
+    "    before_comma, after_comma = i1.split(',')\n",
+    "    before_comma = int(before_comma)\n",
+    "    after_comma = int(after_comma)\n",
+    "    a2.append(before_comma)\n",
+    "    a3.append(after_comma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2['final']=a3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2['lable']=1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2=df2.drop('Measurement',axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>final</th>\n",
+       "      <th>lable</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>97796364</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>54856364</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>36981818</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>54445455</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>21161818</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3698182</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>10683636</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2938</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>104576364</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>135805455</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>124505455</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>6102</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>3698182</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>27736364</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>4930909</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>4930909</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>5547273</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>13149091</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>3492727</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>48281818</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        final  lable\n",
+       "0    97796364      1\n",
+       "1    54856364      1\n",
+       "2    36981818      1\n",
+       "3    54445455      1\n",
+       "4    21161818      1\n",
+       "5     3698182      1\n",
+       "6    10683636      1\n",
+       "7        2938      1\n",
+       "8   104576364      1\n",
+       "9   135805455      1\n",
+       "10  124505455      1\n",
+       "11       6102      1\n",
+       "12    3698182      1\n",
+       "13   27736364      1\n",
+       "14    4930909      1\n",
+       "15    4930909      1\n",
+       "16    5547273      1\n",
+       "17   13149091      1\n",
+       "18    3492727      1\n",
+       "19   48281818      1"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df=pd.concat([df1, df2], axis=0)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1998, 2)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>final</th>\n",
+       "      <th>lable</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4725455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>106630909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>74169091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>208947273</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>56705455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>175458182</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>51774545</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>41501818</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>24449091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>167445455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>165185455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>22805455</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>468641818</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>242230909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>19723636</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>10889091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>125943636</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>41501818</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>267090909</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>74169091</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        final  lable\n",
+       "0     4725455      0\n",
+       "1   106630909      0\n",
+       "2    74169091      0\n",
+       "3   208947273      0\n",
+       "4    56705455      0\n",
+       "5   175458182      0\n",
+       "6    51774545      0\n",
+       "7    41501818      0\n",
+       "8    24449091      0\n",
+       "9   167445455      0\n",
+       "10  165185455      0\n",
+       "11   22805455      0\n",
+       "12  468641818      0\n",
+       "13  242230909      0\n",
+       "14   19723636      0\n",
+       "15   10889091      0\n",
+       "16  125943636      0\n",
+       "17   41501818      0\n",
+       "18  267090909      0\n",
+       "19   74169091      0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    999\n",
+       "1    999\n",
+       "Name: lable, dtype: int64"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['lable'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>final</th>\n",
+       "      <th>lable</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1.998000e+03</td>\n",
+       "      <td>1998.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.177357e+08</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>1.288797e+08</td>\n",
+       "      <td>0.500125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.130000e+02</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.660636e+07</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>7.560727e+07</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.611277e+08</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>8.442127e+08</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              final        lable\n",
+       "count  1.998000e+03  1998.000000\n",
+       "mean   1.177357e+08     0.500000\n",
+       "std    1.288797e+08     0.500125\n",
+       "min    1.130000e+02     0.000000\n",
+       "25%    2.660636e+07     0.000000\n",
+       "50%    7.560727e+07     0.500000\n",
+       "75%    1.611277e+08     1.000000\n",
+       "max    8.442127e+08     1.000000"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 1998 entries, 0 to 998\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column  Non-Null Count  Dtype\n",
+      "---  ------  --------------  -----\n",
+      " 0   final   1998 non-null   int64\n",
+      " 1   lable   1998 non-null   int64\n",
+      "dtypes: int64(2)\n",
+      "memory usage: 46.8 KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train,X_test,y_train,y_test=train_test_split(df['final'],df['lable'],test_size=0.15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train1=np.array(X_train).reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test1=np.array(X_test).reshape(-1,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, f1_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score(X_test,model):\n",
+    "  model.fit(X_train1,y_train)\n",
+    "  y_pred=model.predict(X_test)\n",
+    "  a=accuracy_score(y_test,y_pred)*100\n",
+    "  return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import AdaBoostClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "52.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = LogisticRegression(random_state=0,solver='saga',penalty='l1',max_iter=10000)\n",
+    "v=score(X_test1,model)\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "75.66666666666667"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adaboost = AdaBoostClassifier(n_estimators=1000, random_state=0)\n",
+    "score(X_test1,adaboost)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "73.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "gnb = GaussianNB()\n",
+    "v=score(X_test1,gnb)\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "74.33333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "random_forest = RandomForestClassifier(n_estimators=1000, max_depth=10, random_state=0)\n",
+    "v=score(X_test1,random_forest)\n",
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: xgboost in c:\\users\\ysach\\anaconda3\\lib\\site-packages (2.0.3)\n",
+      "Requirement already satisfied: scipy in c:\\users\\ysach\\anaconda3\\lib\\site-packages (from xgboost) (1.10.0)\n",
+      "Requirement already satisfied: numpy in c:\\users\\ysach\\anaconda3\\lib\\site-packages (from xgboost) (1.23.5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install xgboost"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.77\n"
+     ]
+    }
+   ],
+   "source": [
+    "from xgboost import XGBClassifier\n",
+    "model =XGBClassifier(\n",
+    "    objective='binary:logistic',\n",
+    "    eval_metric='auc',\n",
+    "    learning_rate=0.05,\n",
+    "    max_depth=10,\n",
+    "    n_estimators=1000,\n",
+    "    reg_alpha=3\n",
+    ")\n",
+    "model.fit(X_train1, y_train)\n",
+    "y_pred=model.predict(X_test1)\n",
+    "print(accuracy_score(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[10,5])\n",
+    "bars=plt.bar(['LogisticRegression','adaboost','GaussianNB','random_forest','XGBClassifier'],[52.0,75.67,73.0,74.33,77])\n",
+    "for bar in bars:\n",
+    "    yval = bar.get_height()\n",
+    "    plt.text(bar.get_x() + bar.get_width()/2, yval + 1, round(yval, 2), ha='center', va='bottom')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Bearing Classification/README.md b/Bearing Classification/README.md
new file mode 100644
index 000000000..4963f28cb
--- /dev/null
+++ b/Bearing Classification/README.md	
@@ -0,0 +1,68 @@
+# Bearing Classification using ML 
+
+## PROJECT TITLE
+
+Bearing Classification
+
+## GOAL
+
+To identify faulty and healthy bearing. 
+
+## DATASET
+
+The link for the dataset used in this project:  https://www.kaggle.com/datasets/zlemglsmklkaya/healthy-vs-faulty-bearings/data?select=Healthy-bearing.csv
+
+## EDA:
+![Dataset](Images/Input_Dataset.png)
+![EDA](Images/EDA1.png)
+Shape: (1998,2)
+
+## DESCRIPTION
+
+This project aims to identify the faulty and helthy bearings.
+
+## WHAT I HAD DONE
+
+1. Data collection: From the link of the dataset given above. 
+2. Data preprocessing: Preprocessed the data to create valid features.
+3. Model selection: XGBC,Random Forest,Logestic Regression,Gaussian Bayes,AdaBoost Classifier.
+4. Comparative analysis: Compared the accuracy score of all the models.
+
+
+## MODELS SUMMARY
+
+- XGBC
+- Logistic Regression
+- Adaboost Classifier
+- Random Forest Classifier
+- Gaussian Bayes
+
+## LIBRARIES NEEDED
+
+The following libraries are required to run this project:
+
+- matplotlib
+- numpy
+- pandas
+- sklearn
+
+## EVALUATION METRICS
+
+The evaluation metrics I used to assess the models:
+
+- Accuracy 
+
+It is shown using Confusion Matrix in the Images folder
+
+## RESULTS
+Results on Val dataset:
+XGBC: 77%
+Random Forest: 74.33%
+Adaboost: 75.67%
+Logistic Regression: 52%
+Gaussian Bayes: 73%
+
+## CONCLUSION
+Based on results we can draw following conclusions:
+
+1.The XGBC worked the best
\ No newline at end of file