Experiment no.1 Data Acquisition (1).ipynb

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  {
   "cell_type": "markdown",
   "id": "227f56e2",
   "metadata": {},
   "source": [
    "# Data Acquisition"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb9497ce",
   "metadata": {},
   "source": [
    "Data Acquisition = Data Read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf8ca1f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exp no.:1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "45f35f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Aim: To perform operation on Data Acquisition\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "600b3349",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name: Divyani R. Sadafale\n",
    "#Roll no.: 50\n",
    "#Sec: B\n",
    "#Subject: Data Science and Statistics (Lab 1)\n",
    "#Date: 25/07/2023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5acbf590",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing the basic library\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ec4203a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17c013c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\hp\\\\Downloads'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8b9d05be",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\\\Users\\\\hp\\\\Desktop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ac4db33",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"diabetes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8413b43b",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
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       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
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     "metadata": {},
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   "source": [
    "data.head()"
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   "execution_count": 10,
   "id": "28045641",
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   "outputs": [
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       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  "
      ]
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    "data.tail()"
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  {
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   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.191</td>\n",
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       "      <td>74</td>\n",
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       "      <td>0</td>\n",
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      "text/plain": [
       "    Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0             6      148             72             35        0  33.6   \n",
       "1             1       85             66             29        0  26.6   \n",
       "2             8      183             64              0        0  23.3   \n",
       "3             1       89             66             23       94  28.1   \n",
       "4             0      137             40             35      168  43.1   \n",
       "5             5      116             74              0        0  25.6   \n",
       "6             3       78             50             32       88  31.0   \n",
       "7            10      115              0              0        0  35.3   \n",
       "8             2      197             70             45      543  30.5   \n",
       "9             8      125             96              0        0   0.0   \n",
       "10            4      110             92              0        0  37.6   \n",
       "11           10      168             74              0        0  38.0   \n",
       "\n",
       "    DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                      0.627   50        1  \n",
       "1                      0.351   31        0  \n",
       "2                      0.672   32        1  \n",
       "3                      0.167   21        0  \n",
       "4                      2.288   33        1  \n",
       "5                      0.201   30        0  \n",
       "6                      0.248   26        1  \n",
       "7                      0.134   29        0  \n",
       "8                      0.158   53        1  \n",
       "9                      0.232   54        1  \n",
       "10                     0.191   30        0  \n",
       "11                     0.537   34        1  "
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     "metadata": {},
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