bl.en.u4cse21199 lab1.ipynb

{
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>Candies</th>\n",
       "      <th>Mangoes</th>\n",
       "      <th>Milk Packets</th>\n",
       "      <th>Payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C_1</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C_2</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C_3</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C_4</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C_5</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>C_6</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>C_7</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C_8</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>C_9</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>C_10</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer  Candies  Mangoes  Milk Packets  Payment\n",
       "0      C_1       20        6             2      386\n",
       "1      C_2       16        3             6      289\n",
       "2      C_3       27        6             2      393\n",
       "3      C_4       19        1             2      110\n",
       "4      C_5       24        4             2      280\n",
       "5      C_6       22        1             5      167\n",
       "6      C_7       15        4             2      271\n",
       "7      C_8       18        4             2      274\n",
       "8      C_9       21        1             4      148\n",
       "9     C_10       16        2             4      198"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dat=pd.read_csv('book.csv')\n",
    "dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32162941-6f04-466c-a545-6acc89a01285",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Candies</th>\n",
       "      <th>Mangoes</th>\n",
       "      <th>Milk Packets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
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       "      <th>5</th>\n",
       "      <td>22</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Candies  Mangoes  Milk Packets\n",
       "0       20        6             2\n",
       "1       16        3             6\n",
       "2       27        6             2\n",
       "3       19        1             2\n",
       "4       24        4             2\n",
       "5       22        1             5\n",
       "6       15        4             2\n",
       "7       18        4             2\n",
       "8       21        1             4\n",
       "9       16        2             4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dat)\n",
    "selected_columns = ['Candies', 'Mangoes','Milk Packets']\n",
    "selected_df = df[selected_columns]\n",
    "selected_df"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
   "id": "8b0bfe04-c4bf-4807-912a-6db224200c9e",
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   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>386</td>\n",
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       "      <td>289</td>\n",
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       "      <td>393</td>\n",
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       "      <td>110</td>\n",
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       "      <td>280</td>\n",
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       "      <th>5</th>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
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       "      <th>6</th>\n",
       "      <td>271</td>\n",
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      "text/plain": [
       "   Payment\n",
       "0      386\n",
       "1      289\n",
       "2      393\n",
       "3      110\n",
       "4      280\n",
       "5      167\n",
       "6      271\n",
       "7      274\n",
       "8      148\n",
       "9      198"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(dat)\n",
    "selected_columns1 = ['Payment']\n",
    "selected_df1 = df[selected_columns1]\n",
    "selected_df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05fcab9e-65be-4608-a148-9111871d3afc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20,  6,  2],\n",
       "       [16,  3,  6],\n",
       "       [27,  6,  2],\n",
       "       [19,  1,  2],\n",
       "       [24,  4,  2],\n",
       "       [22,  1,  5],\n",
       "       [15,  4,  2],\n",
       "       [18,  4,  2],\n",
       "       [21,  1,  4],\n",
       "       [16,  2,  4]], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = selected_df.to_numpy()\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2a3db32f-84a4-4d98-bd48-bf0d384746d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[386],\n",
       "       [289],\n",
       "       [393],\n",
       "       [110],\n",
       "       [280],\n",
       "       [167],\n",
       "       [271],\n",
       "       [274],\n",
       "       [148],\n",
       "       [198]], dtype=int64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = selected_df1.to_numpy()\n",
    "C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d515f890-7fee-4bdb-a4f7-e826a4dd5007",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension of the vector space: 3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "num_features = A.shape[1]\n",
    "#What is the dimensionality of the vector space for this data?\n",
    "print(\"Dimension of the vector space:\", num_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f8f875e-8560-4713-97f4-bf1c7f2ea6a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of vectors: 10\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_vectors = A.shape[0]\n",
    "#How many vectors exist in this vector space?\n",
    "print(\"Number of vectors:\", num_vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "959f3bb1-49e4-436b-a95e-4c0baeede1b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row Echelon Form:\n",
      "[[-0.31419211  0.49892601  0.01465039]\n",
      " [-0.25135369  0.07630841  0.77552812]\n",
      " [-0.42415935  0.29691232 -0.28313468]\n",
      " [-0.29848251 -0.36897154 -0.32288052]\n",
      " [-0.37703053  0.02478696 -0.30754116]\n",
      " [-0.34561132 -0.45554884  0.16356567]\n",
      " [-0.23564408  0.28451884  0.07532536]\n",
      " [-0.2827729   0.19794155 -0.05229682]\n",
      " [-0.32990172 -0.42668974  0.00141694]\n",
      " [-0.25135369 -0.10304292  0.29013489]]\n",
      "Rank: 3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "row_echelon, _ = np.linalg.qr(A)\n",
    "\n",
    "# Count the number of non-zero rows to find the rank\n",
    "rank = np.linalg.matrix_rank(row_echelon)\n",
    "#what is the rank of Matrix A?\n",
    "print(\"Row Echelon Form:\")\n",
    "print(row_echelon)\n",
    "print(\"Rank:\", rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "91c64c77-594e-4a39-af0e-65b6ed5c756e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Matrix:\n",
      "[[20  6  2]\n",
      " [16  3  6]\n",
      " [27  6  2]\n",
      " [19  1  2]\n",
      " [24  4  2]\n",
      " [22  1  5]\n",
      " [15  4  2]\n",
      " [18  4  2]\n",
      " [21  1  4]\n",
      " [16  2  4]]\n",
      "Pseudoinverse:\n",
      "[[-0.01008596 -0.03124505  0.01013951  0.0290728   0.0182907   0.01161794\n",
      "  -0.00771348  0.00095458  0.01743623 -0.00542016]\n",
      " [ 0.09059668  0.07263726  0.03172933 -0.09071908 -0.01893196 -0.06926996\n",
      "   0.05675464  0.03152577 -0.07641966  0.00357352]\n",
      " [ 0.00299878  0.15874243 -0.05795468 -0.06609024 -0.06295043  0.03348017\n",
      "   0.01541831 -0.01070461  0.00029003  0.05938755]]\n"
     ]
    }
   ],
   "source": [
    "pseudo_inverse = np.linalg.pinv(A)\n",
    "#Using Pseudo-Inverse find the cost of each product available for sale. \n",
    "print(\"Original Matrix:\")\n",
    "print(A)\n",
    "print(\"Pseudoinverse:\")\n",
    "print(pseudo_inverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2562b35e-353a-44d4-9e86-825bf5f975ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matrix A:\n",
      "[[20  6  2]\n",
      " [16  3  6]\n",
      " [27  6  2]\n",
      " [19  1  2]\n",
      " [24  4  2]\n",
      " [22  1  5]\n",
      " [15  4  2]\n",
      " [18  4  2]\n",
      " [21  1  4]\n",
      " [16  2  4]]\n",
      "Matrix C:\n",
      "[[386]\n",
      " [289]\n",
      " [393]\n",
      " [110]\n",
      " [280]\n",
      " [167]\n",
      " [271]\n",
      " [274]\n",
      " [148]\n",
      " [198]]\n",
      "Model Vector X:\n",
      "[[ 1.]\n",
      " [55.]\n",
      " [18.]]\n"
     ]
    }
   ],
   "source": [
    "#Use the Pseudo-inverse to calculate the model vector X for predicting the cost of the products available with the vendor.\n",
    "X = np.dot(pseudo_inverse, C)\n",
    "print(\"Matrix A:\")\n",
    "print(A)\n",
    "print(\"Matrix C:\")\n",
    "print(C)\n",
    "print(\"Model Vector X:\")\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e8d1321f-30e6-4bb4-854f-c73853336fca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Customer  Candies  Mangoes  Milk Packets  Payment Purchase Category\n",
      "0      C_1       20        6             2      386              Rich\n",
      "1      C_2       16        3             6      289              Rich\n",
      "2      C_3       27        6             2      393              Rich\n",
      "3      C_4       19        1             2      110              Poor\n",
      "4      C_5       24        4             2      280              Rich\n",
      "5      C_6       22        1             5      167              Poor\n",
      "6      C_7       15        4             2      271              Rich\n",
      "7      C_8       18        4             2      274              Rich\n",
      "8      C_9       21        1             4      148              Poor\n",
      "9     C_10       16        2             4      198              Poor\n"
     ]
    }
   ],
   "source": [
    "#Mark all customers(in “Purchase Data” table)with paymentsabove Rs. 200 as RICH and others as POOR. \n",
    "#Develop a classifier model to categorize customers into RICH or POOR class based on purchase behavior.\n",
    "dat['Purchase Category'] = dat['Payment'].apply(lambda x: 'Rich' if x > 200 else 'Poor')\n",
    "print(dat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3129e535-d006-421b-b573-428135cf2e10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n",
      "New customer is: POOR\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\SNS\\AppData\\Roaming\\Python\\Python311\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dat['Category'] =dat['Payment'].apply(lambda x: 'RICH' if x > 200 else 'POOR')\n",
    "\n",
    "Y= dat.iloc[:,1:4]\n",
    "Z= dat['Category']\n",
    "\n",
    "Y_train, Y_test, Z_train, Z_test = train_test_split(Y,Z,test_size=0.25,random_state=0)\n",
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(Y_train,Z_train)\n",
    "\n",
    "print(f'Accuracy: {classifier.score(Y_test,Z_test)}')\n",
    "\n",
    "new_customer = [[1,1,1]]\n",
    "result= classifier.predict(new_customer)\n",
    "print(f'New customer is: {result[0]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0df7eaaa-c9a9-4092-9525-5e8d80b7a4da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Price: 1560.663453815261\n",
      "Variance of Price: 58732.365352539186\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statistics\n",
    "\n",
    "data = pd.read_excel(\"data.xlsx\", sheet_name=\"IRCTC Stock Price\")\n",
    "\n",
    "price_mean = statistics.mean(data[\"Price\"])\n",
    "price_variance = statistics.variance(data[\"Price\"])\n",
    "\n",
    "print(\"Mean Price:\", price_mean)\n",
    "print(\"Variance of Price:\", price_variance)\n",
    "#Calculate the mean and variance of the Price data present in column D."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5d741711",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample_mean_wednesday= 1550.706\n"
     ]
    }
   ],
   "source": [
    "#Select the price data for all Wednesdays and calculate the sample mean. Compare the mean with the population mean and note your observations.\n",
    "wednesday_data = data[data[\"Day\"] == \"Wed\"]\n",
    "sample_mean_wednesday = statistics.mean(wednesday_data[\"Price\"])\n",
    "print(\"Sample_mean_wednesday=\",sample_mean_wednesday)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f169373",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample_mean_april= 1698.9526315789474\n"
     ]
    }
   ],
   "source": [
    "#Select the price data for the month of Apr and calculate the sample mean. Compare the mean with the population mean and note your observations.\n",
    "april_data = data[data[\"Month\"] == \"Apr\"]\n",
    "sample_mean_april = statistics.mean(april_data[\"Price\"])\n",
    "print(\"sample_mean_april=\",sample_mean_april)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11c976e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss_probability= 0.4979919678714859\n"
     ]
    }
   ],
   "source": [
    "#From the Chg% (available in column I) find the probability of making a loss over the stock.\n",
    "loss_probability = len(data[data[\"Chg%\"] < 0]) / len(data)\n",
    "print(\"loss_probability=\",loss_probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "704ee84c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "profit_probability_wednesday= 0.42\n"
     ]
    }
   ],
   "source": [
    "profit_probability_wednesday = len(wednesday_data[wednesday_data[\"Chg%\"] > 0]) / len(wednesday_data)\n",
    "print(\"profit_probability_wednesday=\",profit_probability_wednesday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68f8531b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conditional_prob= 0.42\n"
     ]
    }
   ],
   "source": [
    "conditional_prob = len(wednesday_data[(wednesday_data[\"Chg%\"] > 0)]) / len(wednesday_data)\n",
    "print(\"conditional_prob=\",conditional_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a7769419",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "days = [\"Mon\", \"Tue\", \"Wed\", \"Thu\", \"Fri\"]\n",
    "data[\"DayNumber\"] = data[\"Day\"].apply(lambda x: days.index(x))\n",
    "plt.scatter(data[\"DayNumber\"], data[\"Chg%\"])\n",
    "plt.xticks(range(5), days)\n",
    "plt.xlabel(\"Day of the Week\")\n",
    "plt.ylabel(\"Chg%\")\n",
    "plt.title(\"Chg% vs. Day of the Week\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}