Project_13_7_Feature Engineering Assignment - 5.ipynb

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   "cell_type": "markdown",
   "id": "005aa1e7",
   "metadata": {},
   "source": [
    "## Q1. What is the difference between Ordinal Encoding and Label Encoding? Provide an example of when you might choose one over the other."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41ee7a3",
   "metadata": {},
   "source": [
    "Ordinal Encoding and Label Encoding are both techniques used to convert categorical data into numerical form, but they differ in their application and suitability for different types of categorical variables.\n",
    "\n",
    "#### Ordinal Encoding:\n",
    "\n",
    "- Ordinal Encoding is used when the categorical variable has an inherent order or ranking among its categories.\n",
    "- It assigns numerical values to the categories based on their order, preserving the ordinal relationship.\n",
    "- Typically, the categories are mapped to integer values starting from 0 to N-1, where N is the number of unique categories.\n",
    "- Ordinal Encoding is suitable for ordinal data, where the categories have a meaningful ranking.\n",
    "- Example: If we have a categorical variable representing education level with categories \"High School,\" \"Bachelor's,\" \"Master's,\" and \"Ph.D.,\" we can use ordinal encoding to map them to 0, 1, 2, and 3, respectively.\n",
    "\n",
    "#### Label Encoding:\n",
    "\n",
    "- Label Encoding is used when the categorical variable is nominal, meaning there is no inherent order or ranking among the categories.\n",
    "- It assigns unique numerical labels to each category, effectively creating a nominal-to-numeric mapping.\n",
    "- Label Encoding does not preserve any ordinal relationship among the categories.\n",
    "- It is suitable for nominal data, where the categories do not have any meaningful ranking.\n",
    "- Example: If we have a categorical variable representing colors with categories \"Red,\" \"Blue,\" and \"Green,\" we can use label encoding to map them to 0, 1, and 2, respectively.\n",
    "\n",
    "When there is a rank to be assigned to the data like levels of eductaion background,contract type we use ordinal encoding if the data is of no specific rank like the colors,types of furniture,shapes and so on."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84067512",
   "metadata": {},
   "source": [
    "## Q2. Explain how Target Guided Ordinal Encoding works and provide an example of when you might use it in a machine learning project."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05a4eb05",
   "metadata": {},
   "source": [
    "Target Guided Ordinal Encoding is a technique used to encode categorical variables by creating ordinal labels based on the target variable. It is particularly useful when the target variable is binary (e.g., 0 or 1) or ordinal (e.g., low, medium, high). The main idea behind Target Guided Ordinal Encoding is to encode the categorical variable in such a way that the encoding reflects the relationship between the categorical variable and the target variable, thereby capturing useful information for predictive modeling.\n",
    "\n",
    "In Target Guided Ordinal Encoding, we replace each category in the categorical variable with a numerical value based on the mean or median of the target variable for that category. This creates a monotonic relationship between the categorical variable and the target variable, which can improve the predictive power of our model.\n",
    "\n",
    "1. **Calculate Mean/Median/Other Aggregate of the Target Variable for Each Category** :  For each unique category in the categorical variable, calculate the mean, median, or another appropriate aggregate of the target variable. This step involves creating a mapping of each category to the corresponding mean/median/aggregate of the target variable.\n",
    "2. **Ordering the Category based on the Aggregate Values** : Order the categories based on the calculated mean/median/aggregate value. The idea is to assign higher values to categories that have a higher correlation with the target variable and vice versa.\n",
    "3. **Encode the Categories**: Assign ordinal labels to the categories based on their order. The category with the highest aggregate value gets the highest label, and the one with the lowest aggregate value gets the lowest label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0764d435",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>London</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paris</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tokyo</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  price\n",
       "0  New York    200\n",
       "1    London    150\n",
       "2     Paris    300\n",
       "3     Tokyo    250\n",
       "4  New York    180"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Example\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "# create a sample dataframe with a categorical variable and a target variable\n",
    "df = pd.DataFrame({\n",
    "    'city': ['New York', 'London', 'Paris', 'Tokyo', 'New York', 'Paris'],\n",
    "    'price': [200, 150, 300, 250, 180, 320]\n",
    "})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2661290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'London': 150.0, 'New York': 190.0, 'Paris': 310.0, 'Tokyo': 250.0}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_values = df.groupby('city')['price'].mean().to_dict()\n",
    "mean_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7013b660",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['city_encoded'] = df['city'].map(mean_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86b2ae63",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\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>city</th>\n",
       "      <th>price</th>\n",
       "      <th>city_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>New York</td>\n",
       "      <td>200</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>London</td>\n",
       "      <td>150</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paris</td>\n",
       "      <td>300</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tokyo</td>\n",
       "      <td>250</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>New York</td>\n",
       "      <td>180</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Paris</td>\n",
       "      <td>320</td>\n",
       "      <td>310.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  price  city_encoded\n",
       "0  New York    200         190.0\n",
       "1    London    150         150.0\n",
       "2     Paris    300         310.0\n",
       "3     Tokyo    250         250.0\n",
       "4  New York    180         190.0\n",
       "5     Paris    320         310.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c20483",
   "metadata": {},
   "source": [
    "Here we had a data based on city and cost of living of different people . We had to convert the City Categorical Column to Numerical so the best possible method to covert this data is by using Target Guided Ordinal Encoding."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94d6ffdb",
   "metadata": {},
   "source": [
    "## Q3. Define covariance and explain why it is important in statistical analysis. How is covariance calculated?"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "74488ac2",
   "metadata": {},
   "source": [
    "Covariance is a statistical measure that quantifies the degree to which two variables change together. It indicates the direction of the relationship between two variables and whether they tend to increase or decrease simultaneously. A positive covariance suggests that the variables tend to increase or decrease together, while a negative covariance suggests that as one variable increases, the other decreases.\n",
    "\n",
    "Importance of Covariance in Statistical Analysis:\n",
    "\n",
    "1. **Relationship Assessment**: Covariance helps in understanding the relationship between two variables. If the covariance is positive, it indicates a positive correlation, suggesting that the variables move in the same direction. If it is negative, it indicates a negative correlation, suggesting that the variables move in opposite directions.\n",
    "\n",
    "2. **Data Understanding**: Covariance provides insights into the direction and strength of association between variables, which is crucial for understanding the underlying patterns in the data.\n",
    "\n",
    "3. **Portfolio Diversification**: In finance, covariance is used to analyze the risk and return of portfolios. Covariance between the returns of different assets helps investors diversify their portfolio to manage risk effectively.\n",
    "\n",
    "4. **Modeling**: Covariance is a fundamental component in various statistical models and machine learning algorithms, such as linear regression, principal component analysis (PCA), and factor analysis."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "94a73a99",
   "metadata": {},
   "source": [
    "![Screenshot%202023-08-03%20at%2010.29.25%20PM.png](attachment:Screenshot%202023-08-03%20at%2010.29.25%20PM.png)"
   ]
  },
  {
   "attachments": {
    "Covar.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "c5cb2bdf",
   "metadata": {},
   "source": [
    "![Covar.png](attachment:Covar.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7d31773c",
   "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>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    Male  \n",
       "1       3800.0  Female  \n",
       "2       3250.0  Female  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "df = sns.load_dataset(\"penguins\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cab24555",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pt/19ssfcm95454p139wbpvht400000gn/T/ipykernel_8135/1545644723.py:1: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.cov()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bill_length_mm</th>\n",
       "      <td>29.807054</td>\n",
       "      <td>-2.534234</td>\n",
       "      <td>50.375765</td>\n",
       "      <td>2605.591912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <td>-2.534234</td>\n",
       "      <td>3.899808</td>\n",
       "      <td>-16.212950</td>\n",
       "      <td>-747.370093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <td>50.375765</td>\n",
       "      <td>-16.212950</td>\n",
       "      <td>197.731792</td>\n",
       "      <td>9824.416062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body_mass_g</th>\n",
       "      <td>2605.591912</td>\n",
       "      <td>-747.370093</td>\n",
       "      <td>9824.416062</td>\n",
       "      <td>643131.077327</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "bill_length_mm          29.807054      -2.534234          50.375765   \n",
       "bill_depth_mm           -2.534234       3.899808         -16.212950   \n",
       "flipper_length_mm       50.375765     -16.212950         197.731792   \n",
       "body_mass_g           2605.591912    -747.370093        9824.416062   \n",
       "\n",
       "                     body_mass_g  \n",
       "bill_length_mm       2605.591912  \n",
       "bill_depth_mm        -747.370093  \n",
       "flipper_length_mm    9824.416062  \n",
       "body_mass_g        643131.077327  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d333b8c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pt/19ssfcm95454p139wbpvht400000gn/T/ipykernel_8135/1928163937.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr(method='pearson')\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bill_length_mm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.235053</td>\n",
       "      <td>0.656181</td>\n",
       "      <td>0.595110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <td>-0.235053</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.583851</td>\n",
       "      <td>-0.471916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <td>0.656181</td>\n",
       "      <td>-0.583851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.871202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body_mass_g</th>\n",
       "      <td>0.595110</td>\n",
       "      <td>-0.471916</td>\n",
       "      <td>0.871202</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "bill_length_mm           1.000000      -0.235053           0.656181   \n",
       "bill_depth_mm           -0.235053       1.000000          -0.583851   \n",
       "flipper_length_mm        0.656181      -0.583851           1.000000   \n",
       "body_mass_g              0.595110      -0.471916           0.871202   \n",
       "\n",
       "                   body_mass_g  \n",
       "bill_length_mm        0.595110  \n",
       "bill_depth_mm        -0.471916  \n",
       "flipper_length_mm     0.871202  \n",
       "body_mass_g           1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6a3adf1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pt/19ssfcm95454p139wbpvht400000gn/T/ipykernel_8135/1222267885.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr(method='spearman')\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bill_length_mm</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.672772</td>\n",
       "      <td>0.583800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <td>-0.221749</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.523267</td>\n",
       "      <td>-0.432372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <td>0.672772</td>\n",
       "      <td>-0.523267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.839974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>body_mass_g</th>\n",
       "      <td>0.583800</td>\n",
       "      <td>-0.432372</td>\n",
       "      <td>0.839974</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "bill_length_mm           1.000000      -0.221749           0.672772   \n",
       "bill_depth_mm           -0.221749       1.000000          -0.523267   \n",
       "flipper_length_mm        0.672772      -0.523267           1.000000   \n",
       "body_mass_g              0.583800      -0.432372           0.839974   \n",
       "\n",
       "                   body_mass_g  \n",
       "bill_length_mm        0.583800  \n",
       "bill_depth_mm        -0.432372  \n",
       "flipper_length_mm     0.839974  \n",
       "body_mass_g           1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead45caf",
   "metadata": {},
   "source": [
    "## Q4. For a dataset with the following categorical variables: Color (red, green, blue), Size (small, medium, large), and Material (wood, metal, plastic), perform label encoding using Python's scikit-learn library. Show your code and explain the output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9f7e66eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Color</th>\n",
       "      <th>Size</th>\n",
       "      <th>Material</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
       "      <td>small</td>\n",
       "      <td>wood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>green</td>\n",
       "      <td>large</td>\n",
       "      <td>wood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>medium</td>\n",
       "      <td>plastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>medium</td>\n",
       "      <td>plastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>green</td>\n",
       "      <td>medium</td>\n",
       "      <td>metal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Color    Size Material\n",
       "0    red   small     wood\n",
       "1  green   large     wood\n",
       "2   blue  medium  plastic\n",
       "3    red  medium  plastic\n",
       "4  green  medium    metal"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#For example creating the above Dataset\n",
    "df = pd.DataFrame({\"Color\" : ['red','green','blue','red','green'],\n",
    "            \"Size\" : ['small','large','medium','medium','medium'],\n",
    "            \"Material\" : ['wood','wood','plastic','plastic','metal']})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "336603de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Color</th>\n",
       "      <th>Size</th>\n",
       "      <th>Material</th>\n",
       "      <th>color_encoded</th>\n",
       "      <th>material_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
       "      <td>small</td>\n",
       "      <td>wood</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>green</td>\n",
       "      <td>large</td>\n",
       "      <td>wood</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>medium</td>\n",
       "      <td>plastic</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>medium</td>\n",
       "      <td>plastic</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>green</td>\n",
       "      <td>medium</td>\n",
       "      <td>metal</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Color    Size Material  color_encoded  material_encoded\n",
       "0    red   small     wood              2                 2\n",
       "1  green   large     wood              1                 2\n",
       "2   blue  medium  plastic              0                 1\n",
       "3    red  medium  plastic              2                 1\n",
       "4  green  medium    metal              1                 0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Color & Material Coloumn we will use Label Encoding \n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#creating different objects for each coloumn so there is no overridding when new data is inputted\n",
    "encode= LabelEncoder()\n",
    "encodee = LabelEncoder()\n",
    "df['color_encoded'] = pd.DataFrame(encode.fit_transform(df['Color']))\n",
    "df['material_encoded']= pd.DataFrame(encodee.fit_transform(df['Material']))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "40536fc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode.transform(['red'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "18bbacfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encodee.transform(['plastic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5bd36770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Color</th>\n",
       "      <th>Size</th>\n",
       "      <th>Material</th>\n",
       "      <th>color_encoded</th>\n",
       "      <th>material_encoded</th>\n",
       "      <th>size_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>red</td>\n",
       "      <td>small</td>\n",
       "      <td>wood</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>green</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blue</td>\n",
       "      <td>medium</td>\n",
       "      <td>plastic</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>red</td>\n",
       "      <td>medium</td>\n",
       "      <td>plastic</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>green</td>\n",
       "      <td>medium</td>\n",
       "      <td>metal</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Color    Size Material  color_encoded  material_encoded  size_encoded\n",
       "0    red   small     wood              2                 2           0.0\n",
       "1  green   large     wood              1                 2           2.0\n",
       "2   blue  medium  plastic              0                 1           1.0\n",
       "3    red  medium  plastic              2                 1           1.0\n",
       "4  green  medium    metal              1                 0           1.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For Size Coloumn we will use Ordinal Encoding\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "encode_obj = OrdinalEncoder(categories=[['small','medium','large']])\n",
    "df['size_encoded'] = pd.DataFrame(encode_obj.fit_transform(df[['Size']]))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa8ac252",
   "metadata": {},
   "source": [
    "Here we have used Label Encoding for color and material columns and ordinal encoding for size column .If additional data regarding price was given then we could have done target based encoding for the material column."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86902b4",
   "metadata": {},
   "source": [
    "## Q5. Calculate the covariance matrix for the following variables in a dataset: Age, Income, and Education level. Interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8c4b8c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education Level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>50000</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>60000</td>\n",
       "      <td>Master</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>39000</td>\n",
       "      <td>High School</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>55000</td>\n",
       "      <td>Bachelor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>35000</td>\n",
       "      <td>High School</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Income Education Level\n",
       "0   30   50000        Bachelor\n",
       "1   40   60000          Master\n",
       "2   22   39000     High School\n",
       "3   35   55000        Bachelor\n",
       "4   21   35000     High School"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Sample dataset as a dictionary\n",
    "df = pd.DataFrame({\n",
    "    'Age': [30, 40, 22, 35, 21],\n",
    "    'Income': [50000, 60000, 39000, 55000, 35000],\n",
    "    'Education Level': ['Bachelor', 'Master', 'High School', 'Bachelor', 'High School']\n",
    "    })\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b9f2fd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/pt/19ssfcm95454p139wbpvht400000gn/T/ipykernel_8135/1545644723.py:1: FutureWarning: The default value of numeric_only in DataFrame.cov is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.cov()\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>67.3</td>\n",
       "      <td>85900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>85900.0</td>\n",
       "      <td>111700000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Age       Income\n",
       "Age        67.3      85900.0\n",
       "Income  85900.0  111700000.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cov()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39f106c4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b10867b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>Education Level</th>\n",
       "      <th>education_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>50000</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>60000</td>\n",
       "      <td>Master</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>39000</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>55000</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>35000</td>\n",
       "      <td>High School</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Income Education Level  education_encoded\n",
       "0   30   50000        Bachelor                1.0\n",
       "1   40   60000          Master                2.0\n",
       "2   22   39000     High School                0.0\n",
       "3   35   55000        Bachelor                1.0\n",
       "4   21   35000     High School                0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#So converting the Education Level using ordinal encoding\n",
    "\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "\n",
    "encoding_obj = OrdinalEncoder(categories=[['High School','Bachelor','Master']])\n",
    "df['education_encoded'] = encoding_obj.fit_transform(df[['Education Level']])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a596c06b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>education_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30</td>\n",
       "      <td>50000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>40</td>\n",
       "      <td>60000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>39000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35</td>\n",
       "      <td>55000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>35000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Income  education_encoded\n",
       "0   30   50000                1.0\n",
       "1   40   60000                2.0\n",
       "2   22   39000                0.0\n",
       "3   35   55000                1.0\n",
       "4   21   35000                0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns='Education Level',inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f45dc33",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Age</th>\n",
       "      <th>Income</th>\n",
       "      <th>education_encoded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>67.30</td>\n",
       "      <td>85900.0</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Income</th>\n",
       "      <td>85900.00</td>\n",
       "      <td>111700000.0</td>\n",
       "      <td>8450.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>education_encoded</th>\n",
       "      <td>6.65</td>\n",
       "      <td>8450.0</td>\n",
       "      <td>0.70</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Age       Income  education_encoded\n",
       "Age                   67.30      85900.0               6.65\n",
       "Income             85900.00  111700000.0            8450.00\n",
       "education_encoded      6.65       8450.0               0.70"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Covariance Matrix\n",
    "df.cov()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05b81f84",
   "metadata": {},
   "source": [
    "Each row and column in the matrix corresponds to one of these variables, and the elements represent the covariance between pairs of variables.\n",
    "\n",
    "The covariance matrix is symmetric, as the covariance between X and Y is the same as the covariance between Y and X. \n",
    "\n",
    "Also, the diagonal elements represent the variance of each variable, which is the covariance of a variable with itself.\n",
    "\n",
    "Interpretation of the covariance matrix:\n",
    "- The variance of Age is approximately 67.30.\n",
    "- The variance of Income is approximately 111,700,000.0.\n",
    "- The variance of education_encoded is approximately 0.70.\n",
    "\n",
    "\n",
    "Additionally, the off-diagonal elements represent the covariance between pairs of variables:\n",
    "\n",
    "- The covariance between Age and Income is approximately 85,900.0. It indicates a positive relationship, suggesting that as Age increases, Income tends to increase as well (they move together).\n",
    "- The covariance between Age and education_encoded is approximately 6.65. It indicates a positive relationship, suggesting that as Age increases, education_encoded tends to increase as well (they move together).\n",
    "- The covariance between Income and education_encoded is approximately 8,450.0. It indicates a positive relationship, suggesting that higher Income is associated with higher education_encoded (they move together).\n",
    "- The covariance matrix provides insights into the relationships between the variables and the variability of each variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e583efa2",
   "metadata": {},
   "source": [
    "## Q6. You are working on a machine learning project with a dataset containing several categorical variables, including \"Gender\" (Male/Female), \"Education Level\" (High School/Bachelor's/Master's/PhD), and \"Employment Status\" (Unemployed/Part-Time/Full-Time). Which encoding method would you use for each variable, and why?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb1cfb58",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "**Gender (Binary Categorical Variable: Male/Female)**:\n",
    "Since \"Gender\" is a binary categorical variable with only two possible values (Male and Female), the preferred encoding method is Label Encoding. In label encoding, we can assign 0 to one category (e.g., Male) and 1 to the other category (e.g., Female). Label encoding is suitable for binary categorical variables as it allows us to represent the categories as numerical values, which is useful for various machine learning algorithms.\n",
    "\n",
    "**Education Level (Ordinal Categorical Variable: High School/Bachelor's/Master's/PhD)**:\n",
    "\"Education Level\" is an ordinal categorical variable with a clear ordering of categories. In this case, the recommended encoding method is Ordinal Encoding. Ordinal encoding assigns a unique integer value to each category based on its order. For example, we can encode High School as 0, Bachelor's as 1, Master's as 2, and PhD as 3. By using ordinal encoding, we preserve the ordinal relationship between the categories, which is important when certain categories have a natural order.\n",
    "\n",
    "**Employment Status (Nominal Categorical Variable: Unemployed/Part-Time/Full-Time)**:\n",
    "\"Employment Status\" is a nominal categorical variable with no inherent order among its categories. For nominal categorical variables, the preferred encoding method is One-Hot Encoding. One-hot encoding creates binary columns for each category, where a value of 1 represents the presence of the category, and 0 represents its absence. For example, we can create three columns (Unemployed, Part-Time, Full-Time) where the corresponding category is encoded as 1 and the others as 0. One-hot encoding is useful for preventing any ordinality between categories, as they are treated as distinct and independent.\n",
    "\n",
    "\n",
    "- Nominal for Gender as they are binary category variables\n",
    "- Ordinal for Education Level as they are ordinal category variables\n",
    "- One hot Encoding for Employment Status as they are nominal category variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7259a01d",
   "metadata": {},
   "source": [
    "## Q7. You are analyzing a dataset with two continuous variables, \"Temperature\" and \"Humidity\", and two categorical variables, \"Weather Condition\" (Sunny/Cloudy/Rainy) and \"Wind Direction\" (North/South/ East/West). Calculate the covariance between each pair of variables and interpret the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "595b3d73",
   "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",
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       "    }\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>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Weather Condition</th>\n",
       "      <th>Wind Direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>South</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>55</td>\n",
       "      <td>Rainy</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>75</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>65</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>North</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Humidity Weather Condition Wind Direction\n",
       "0           25        60             Sunny          North\n",
       "1           20        70            Cloudy          South\n",
       "2           30        55             Rainy           East\n",
       "3           22        75             Sunny           West\n",
       "4           28        65            Cloudy          North"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'Temperature': [25, 20, 30, 22, 28],\n",
    "    'Humidity': [60, 70, 55, 75, 65],\n",
    "    'Weather Condition': ['Sunny', 'Cloudy', 'Rainy', 'Sunny', 'Cloudy'],\n",
    "    'Wind Direction': ['North', 'South', 'East', 'West', 'North']\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6c2c2ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting Categorial Variables to Numerical as coariance only works on numerical data\n",
    "#Using OHE for Weather Condition & Wind Direction as they are Binary Variables\n",
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "encodeee= OneHotEncoder()\n",
    "values = encodeee.fit_transform(df[['Weather Condition','Wind Direction']]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "98270aff",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Weather Condition_Cloudy</th>\n",
       "      <th>Weather Condition_Rainy</th>\n",
       "      <th>Weather Condition_Sunny</th>\n",
       "      <th>Wind Direction_East</th>\n",
       "      <th>Wind Direction_North</th>\n",
       "      <th>Wind Direction_South</th>\n",
       "      <th>Wind Direction_West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25</td>\n",
       "      <td>60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>70</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22</td>\n",
       "      <td>75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>65</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature  Humidity  Weather Condition_Cloudy  Weather Condition_Rainy  \\\n",
       "0           25        60                       0.0                      0.0   \n",
       "1           20        70                       1.0                      0.0   \n",
       "2           30        55                       0.0                      1.0   \n",
       "3           22        75                       0.0                      0.0   \n",
       "4           28        65                       1.0                      0.0   \n",
       "\n",
       "   Weather Condition_Sunny  Wind Direction_East  Wind Direction_North  \\\n",
       "0                      1.0                  0.0                   1.0   \n",
       "1                      0.0                  0.0                   0.0   \n",
       "2                      0.0                  1.0                   0.0   \n",
       "3                      1.0                  0.0                   0.0   \n",
       "4                      0.0                  0.0                   1.0   \n",
       "\n",
       "   Wind Direction_South  Wind Direction_West  \n",
       "0                   0.0                  0.0  \n",
       "1                   1.0                  0.0  \n",
       "2                   0.0                  0.0  \n",
       "3                   0.0                  1.0  \n",
       "4                   0.0                  0.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df = pd.DataFrame(values,columns=encodeee.get_feature_names_out())\n",
    "df = pd.concat([df,encoded_df],axis =1)\n",
    "df.drop(columns=['Weather Condition','Wind Direction'],inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d15e91cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Covariance Matrix : \n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Weather Condition_Cloudy</th>\n",
       "      <th>Weather Condition_Rainy</th>\n",
       "      <th>Weather Condition_Sunny</th>\n",
       "      <th>Wind Direction_East</th>\n",
       "      <th>Wind Direction_North</th>\n",
       "      <th>Wind Direction_South</th>\n",
       "      <th>Wind Direction_West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature</th>\n",
       "      <td>17.00</td>\n",
       "      <td>-26.25</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>-0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity</th>\n",
       "      <td>-26.25</td>\n",
       "      <td>62.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather Condition_Cloudy</th>\n",
       "      <td>-0.50</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather Condition_Rainy</th>\n",
       "      <td>1.25</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather Condition_Sunny</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Direction_East</th>\n",
       "      <td>1.25</td>\n",
       "      <td>-2.50</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Direction_North</th>\n",
       "      <td>0.75</td>\n",
       "      <td>-1.25</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.30</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Direction_South</th>\n",
       "      <td>-1.25</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.20</td>\n",
       "      <td>-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind Direction_West</th>\n",
       "      <td>-0.75</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.15</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Temperature  Humidity  Weather Condition_Cloudy  \\\n",
       "Temperature                     17.00    -26.25                     -0.50   \n",
       "Humidity                       -26.25     62.50                      1.25   \n",
       "Weather Condition_Cloudy        -0.50      1.25                      0.30   \n",
       "Weather Condition_Rainy          1.25     -2.50                     -0.10   \n",
       "Weather Condition_Sunny         -0.75      1.25                     -0.20   \n",
       "Wind Direction_East              1.25     -2.50                     -0.10   \n",
       "Wind Direction_North             0.75     -1.25                      0.05   \n",
       "Wind Direction_South            -1.25      1.25                      0.15   \n",
       "Wind Direction_West             -0.75      2.50                     -0.10   \n",
       "\n",
       "                          Weather Condition_Rainy  Weather Condition_Sunny  \\\n",
       "Temperature                                  1.25                    -0.75   \n",
       "Humidity                                    -2.50                     1.25   \n",
       "Weather Condition_Cloudy                    -0.10                    -0.20   \n",
       "Weather Condition_Rainy                      0.20                    -0.10   \n",
       "Weather Condition_Sunny                     -0.10                     0.30   \n",
       "Wind Direction_East                          0.20                    -0.10   \n",
       "Wind Direction_North                        -0.10                     0.05   \n",
       "Wind Direction_South                        -0.05                    -0.10   \n",
       "Wind Direction_West                         -0.05                     0.15   \n",
       "\n",
       "                          Wind Direction_East  Wind Direction_North  \\\n",
       "Temperature                              1.25                  0.75   \n",
       "Humidity                                -2.50                 -1.25   \n",
       "Weather Condition_Cloudy                -0.10                  0.05   \n",
       "Weather Condition_Rainy                  0.20                 -0.10   \n",
       "Weather Condition_Sunny                 -0.10                  0.05   \n",
       "Wind Direction_East                      0.20                 -0.10   \n",
       "Wind Direction_North                    -0.10                  0.30   \n",
       "Wind Direction_South                    -0.05                 -0.10   \n",
       "Wind Direction_West                     -0.05                 -0.10   \n",
       "\n",
       "                          Wind Direction_South  Wind Direction_West  \n",
       "Temperature                              -1.25                -0.75  \n",
       "Humidity                                  1.25                 2.50  \n",
       "Weather Condition_Cloudy                  0.15                -0.10  \n",
       "Weather Condition_Rainy                  -0.05                -0.05  \n",
       "Weather Condition_Sunny                  -0.10                 0.15  \n",
       "Wind Direction_East                      -0.05                -0.05  \n",
       "Wind Direction_North                     -0.10                -0.10  \n",
       "Wind Direction_South                      0.20                -0.05  \n",
       "Wind Direction_West                      -0.05                 0.20  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Covariance Matrix : \")\n",
    "df.cov()"
   ]
  }
 ],
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