Day 48 - 50 Days of Data Analysis .ipynb

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    "## 2.\t\n",
    "\n",
    "#### Using the lists below create a pandas DataFrame with three columns."
   ]
  },
  {
   "cell_type": "code",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>flower_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width flower_type\n",
       "0           5.1          3.5           1.4          0.2      setosa\n",
       "1           4.9          3.0           1.4          0.2      setosa\n",
       "2           4.7          3.2           1.3          0.2  versicolor\n",
       "3           4.6          3.1           1.5          0.2  versicolor\n",
       "4           5.0          3.6           1.4          0.2      setosa"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"flower_dataset.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac557ea1",
   "metadata": {},
   "source": [
    "## 3.\t\n",
    "\n",
    "#### Create a copy of the DataFrame. In supervised machine learning, the target variable is the variable that will be predicted using the other variables in the dataset. This target variable must be converted into numeric data type before fitting a model. Convert this target variable(flower_type) into numeric data type and separate the target column from the other variables. Write code to create two variables, x and y. X is the variable that will predict the target variable, y.\n",
    "\n",
    "#### Use scikit-learn's train_test_split() function to split the data into training and test sets. Make the test size to be 20% of the data set. Set the random_state parameter to 42. Check the shapes of the training and test sets. What is the purpose of the random_state parameter in the train_test_split() function?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "231bd94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the DataFrame\n",
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "786f6c2f",
   "metadata": {},
   "outputs": [
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>flower_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width  flower_type\n",
       "0           5.1          3.5           1.4          0.2            0\n",
       "1           4.9          3.0           1.4          0.2            0\n",
       "2           4.7          3.2           1.3          0.2            1\n",
       "3           4.6          3.1           1.5          0.2            1\n",
       "4           5.0          3.6           1.4          0.2            0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Initializing label encoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "df_copy[\"flower_type\"] = le.fit_transform(df_copy[\"flower_type\"])\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6cc859e6",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "((16, 4), (16,), (4, 4), (4,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Dropping the target column\n",
    "X = df_copy.drop(columns=[\"flower_type\"])\n",
    "\n",
    "# Separate target column from DataFrame\n",
    "y = df_copy[\"flower_type\"]\n",
    "\n",
    "# Splitting the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=42)\n",
    "\n",
    "# Checking the shape of training and test data set\n",
    "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e7779e",
   "metadata": {},
   "source": [
    "## 4.\t\n",
    "\n",
    "#### Why is it important to standardize the data before fitting? Use Scikit-Learn's StandardScaler to standardize the features in the dataset (training and test sets)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83e7c1a7",
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       "array([[-1.3782598 , -1.53281263, -0.23791548, -0.2773501 ],\n",
       "       [ 0.94301987,  1.25411943,  1.66540833,  1.94145069],\n",
       "       [-0.44974794, -0.1393466 ,  1.03096706, -0.2773501 ],\n",
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       "       [ 0.94301987,  1.25411943, -0.87235674,  1.94145069],\n",
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       "       [-0.21761997, -0.97542622,  0.39652579, -1.38675049],\n",
       "       [ 0.24663596,  0.97542622,  0.39652579,  0.83205029],\n",
       "       [ 0.014508  ,  0.41803981, -0.23791548, -0.2773501 ],\n",
       "       [-0.44974794, -1.25411943, -0.23791548, -1.38675049],\n",
       "       [ 0.014508  , -0.1393466 ,  0.39652579, -0.2773501 ],\n",
       "       [ 0.94301987,  0.69673301,  0.39652579, -0.2773501 ],\n",
       "       [ 1.87153173,  1.53281263, -1.50679801, -0.2773501 ],\n",
       "       [-0.91400387, -0.1393466 , -0.23791548,  0.83205029]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# Scale the data using StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a38f578",
   "metadata": {},
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