{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8579956,"sourceType":"datasetVersion","datasetId":5131123},{"sourceId":8646409,"sourceType":"datasetVersion","datasetId":5178673},{"sourceId":8767723,"sourceType":"datasetVersion","datasetId":5268544},{"sourceId":8797164,"sourceType":"datasetVersion","datasetId":5289783}],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-07-04T16:56:51.637094Z","iopub.execute_input":"2024-07-04T16:56:51.638078Z","iopub.status.idle":"2024-07-04T16:56:52.060609Z","shell.execute_reply.started":"2024-07-04T16:56:51.638042Z","shell.execute_reply":"2024-07-04T16:56:52.059233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"flight = pd.read_csv('/kaggle/input/flight-price-data/flight_dataset.csv')\nweather = pd.read_csv('/kaggle/input/weather-type-classification/weather_classification_data.csv')\nsmartphone = pd.read_csv('/kaggle/input/smartphones-dataset/smartphones_cleaned_v6.csv')\nrestra = pd.read_csv('/kaggle/input/restaurant-revenue-prediction-dataset/restaurant_data.csv')","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:52.062977Z","iopub.execute_input":"2024-07-04T16:56:52.063480Z","iopub.status.idle":"2024-07-04T16:56:52.208879Z","shell.execute_reply.started":"2024-07-04T16:56:52.063437Z","shell.execute_reply":"2024-07-04T16:56:52.207714Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Flight Dataset","metadata":{}},{"cell_type":"code","source":"flight.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:52.210462Z","iopub.execute_input":"2024-07-04T16:56:52.211353Z","iopub.status.idle":"2024-07-04T16:56:52.240137Z","shell.execute_reply.started":"2024-07-04T16:56:52.211306Z","shell.execute_reply":"2024-07-04T16:56:52.239046Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"flight.columns","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:52.241603Z","iopub.execute_input":"2024-07-04T16:56:52.242652Z","iopub.status.idle":"2024-07-04T16:56:52.253808Z","shell.execute_reply.started":"2024-07-04T16:56:52.242607Z","shell.execute_reply":"2024-07-04T16:56:52.252679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt \n\n# Scatter plot - Price vs Month\nplt.figure(figsize=(8, 6))\nplt.scatter(flight['Month'], flight['Price'], color='blue', alpha=0.7)\nplt.xlabel('Month')\nplt.ylabel('Price')\nplt.title('Price Distribution by Month')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:52.257507Z","iopub.execute_input":"2024-07-04T16:56:52.257909Z","iopub.status.idle":"2024-07-04T16:56:52.631896Z","shell.execute_reply.started":"2024-07-04T16:56:52.257878Z","shell.execute_reply":"2024-07-04T16:56:52.630603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Histogram - Departure Delay Distribution\nplt.figure(figsize=(10, 5))\nplt.subplot(1, 2, 1)\nplt.hist(flight['Dep_min'], bins=10, edgecolor='black', alpha=0.7)\nplt.xlabel('Departure Delay (minutes)')\nplt.ylabel('Frequency')\nplt.title('Departure Delay Distribution')\nplt.grid(True)\n\n# Histogram - Arrival Delay Distribution\nplt.subplot(1, 2, 2)\nplt.hist(flight['Arrival_min'], bins=10, edgecolor='black', alpha=0.7)\nplt.xlabel('Arrival Delay (minutes)')\nplt.ylabel('Frequency')\nplt.title('Arrival Delay Distribution')\nplt.grid(True)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:52.633541Z","iopub.execute_input":"2024-07-04T16:56:52.634053Z","iopub.status.idle":"2024-07-04T16:56:53.215593Z","shell.execute_reply.started":"2024-07-04T16:56:52.634010Z","shell.execute_reply":"2024-07-04T16:56:53.214276Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Alternatively, you can use boxplots for a more compact view\nplt.figure(figsize=(8, 6))\nplt.boxplot([flight['Dep_min'], flight['Arrival_min']], labels=['Departure Delay', 'Arrival Delay'], notch=True)\nplt.xlabel('Delay Type')\nplt.ylabel('Delay in minutes')\nplt.title('Distribution of Departure and Arrival Delays')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:53.217857Z","iopub.execute_input":"2024-07-04T16:56:53.218366Z","iopub.status.idle":"2024-07-04T16:56:53.516535Z","shell.execute_reply.started":"2024-07-04T16:56:53.218324Z","shell.execute_reply":"2024-07-04T16:56:53.515419Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"route_durations = flight.groupby(['Source', 'Destination'])['Duration_hours'].sum().reset_index()\nplt.figure(figsize=(10, 6))\nroutes = route_durations['Source'].unique()\nfor i, route in enumerate(routes):\n    route_data = flight[ (flight['Source'] == route) & (flight['Destination'] == route_durations[route_durations['Source'] == route]['Destination'].values[0]) ]\n    plt.boxplot(route_data['Duration_hours'], positions=[i], notch=True, vert=False, labels=[route])\nplt.xlabel('Flight Duration (hours)')\nplt.ylabel('Route')\nplt.title('Distribution of Flight Durations by Route')\nplt.grid(True)\nplt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:53.521515Z","iopub.execute_input":"2024-07-04T16:56:53.521959Z","iopub.status.idle":"2024-07-04T16:56:53.946533Z","shell.execute_reply.started":"2024-07-04T16:56:53.521917Z","shell.execute_reply":"2024-07-04T16:56:53.945371Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10, 6))\nplt.scatter(flight['Airline'], flight['Duration_hours'], color='blue', alpha=0.7)\nplt.xlabel('Airline')\nplt.xticks(rotation=90)\nplt.ylabel('Flight Duration (hours)')\nplt.title('Flight Durations by Airline')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:53.948424Z","iopub.execute_input":"2024-07-04T16:56:53.949043Z","iopub.status.idle":"2024-07-04T16:56:54.399694Z","shell.execute_reply.started":"2024-07-04T16:56:53.949009Z","shell.execute_reply":"2024-07-04T16:56:54.398400Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Weather Dataset","metadata":{}},{"cell_type":"code","source":"weather.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:54.401309Z","iopub.execute_input":"2024-07-04T16:56:54.401693Z","iopub.status.idle":"2024-07-04T16:56:54.424213Z","shell.execute_reply.started":"2024-07-04T16:56:54.401659Z","shell.execute_reply":"2024-07-04T16:56:54.423043Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"weather.columns","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:54.425851Z","iopub.execute_input":"2024-07-04T16:56:54.426570Z","iopub.status.idle":"2024-07-04T16:56:54.441080Z","shell.execute_reply.started":"2024-07-04T16:56:54.426528Z","shell.execute_reply":"2024-07-04T16:56:54.439699Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\n# Define the list of columns to create boxplots for\ncolumns_to_plot = ['Temperature', 'Humidity', 'Wind Speed', 'Precipitation (%)',\n                    'Atmospheric Pressure', 'UV Index', 'Visibility (km)']\n\n# Set the aesthetic style of the plots\nsns.set(style=\"whitegrid\")\n\n# Create a figure and axes for the subplots\nfig, axes = plt.subplots(nrows=len(columns_to_plot), ncols=1, figsize=(10, 5*len(columns_to_plot)))\n\n# Plot each column as a boxplot\nfor i, column in enumerate(columns_to_plot):\n    sns.boxplot(data=weather, x='Season', y=column, ax=axes[i])\n    axes[i].set_title(f'Distribution of {column} Across Seasons')\n    axes[i].set_xlabel('Season')\n    axes[i].set_ylabel(column)\n\n# Adjust the layout to make room for the titles and labels\nplt.tight_layout()\n\n# Show the plots\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:54.443136Z","iopub.execute_input":"2024-07-04T16:56:54.443969Z","iopub.status.idle":"2024-07-04T16:56:58.238953Z","shell.execute_reply.started":"2024-07-04T16:56:54.443924Z","shell.execute_reply":"2024-07-04T16:56:58.237803Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set the aesthetic style of the plots\nsns.set(style=\"whitegrid\")\n\n# Define the pairs to create scatter plots for\nscatter_pairs = [\n    ('Temperature', 'Humidity'),\n    ('Wind Speed', 'Precipitation (%)'),\n    ('Atmospheric Pressure', 'Wind Speed')\n]\n\n# Create a figure and axes for the subplots\nfig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 20))\n\n# Plot each pair as a scatter plot\nfor i, (x, y) in enumerate(scatter_pairs):\n    sns.scatterplot(data=weather, x=x, y=y, ax=axes[i])\n    axes[i].set_title(f'{x} vs. {y}')\n    axes[i].set_xlabel(x)\n    axes[i].set_ylabel(y)\n\n# Adjust the layout to make room for the titles and labels\nplt.tight_layout()\n\n# Show the plots\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:58.240215Z","iopub.execute_input":"2024-07-04T16:56:58.240519Z","iopub.status.idle":"2024-07-04T16:56:59.677424Z","shell.execute_reply.started":"2024-07-04T16:56:58.240492Z","shell.execute_reply":"2024-07-04T16:56:59.676344Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define the list of weather elements to include in the heatmap\nweather_elements = ['Temperature', 'Humidity', 'Wind Speed', 'Precipitation (%)',\n                     'Atmospheric Pressure', 'UV Index', 'Visibility (km)']\n\n# Pivot the DataFrame to have locations as rows and weather elements as columns\nheatmap_data = weather.pivot_table(index='Location', values=weather_elements, aggfunc='mean')\n\n# Set the aesthetic style of the plots\nsns.set(style=\"whitegrid\")\n\n# Create the heatmap\nplt.figure(figsize=(12, 8))\nsns.heatmap(heatmap_data, annot=True, cmap='coolwarm', linewidths=.5)\n\n# Set the title and labels\nplt.title('Heatmap of Weather Elements by Location')\nplt.xlabel('Weather Element')\nplt.ylabel('Location')\n\n# Show the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:56:59.680824Z","iopub.execute_input":"2024-07-04T16:56:59.681209Z","iopub.status.idle":"2024-07-04T16:57:00.171780Z","shell.execute_reply.started":"2024-07-04T16:56:59.681156Z","shell.execute_reply":"2024-07-04T16:57:00.170607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define the list of weather elements to plot\nweather_elements = ['Temperature', 'Humidity', 'Wind Speed', 'Precipitation (%)',\n                     'Atmospheric Pressure', 'UV Index', 'Visibility (km)']\n\n# Calculate the mean of each weather element for each location\nlocation_means = weather.groupby('Location')[weather_elements].mean().reset_index()\n\n# Set the aesthetic style of the plots\nsns.set(style=\"whitegrid\")\n\n# Create a figure and axes for the subplots\nfig, axes = plt.subplots(nrows=len(weather_elements), ncols=1, figsize=(8, 5*len(weather_elements)))\n\n# Plot each weather element as a bar chart\nfor i, element in enumerate(weather_elements):\n    sns.barplot(data=location_means, x='Location', y=element, ax=axes[i])\n    axes[i].set_title(f'Average {element} Across Locations')\n    axes[i].set_xlabel('Location')\n    axes[i].set_ylabel(f'Average {element}')\n    axes[i].tick_params(axis='x', rotation=90)  # Rotate x-axis labels for better readability\n\n# Adjust the layout to make room for the titles and labels\nplt.tight_layout()\n\n# Show the plots\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:57:00.173164Z","iopub.execute_input":"2024-07-04T16:57:00.173540Z","iopub.status.idle":"2024-07-04T16:57:01.997558Z","shell.execute_reply.started":"2024-07-04T16:57:00.173507Z","shell.execute_reply":"2024-07-04T16:57:01.996431Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Smartphone Dataset","metadata":{}},{"cell_type":"code","source":"smartphone.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:57:01.998970Z","iopub.execute_input":"2024-07-04T16:57:01.999380Z","iopub.status.idle":"2024-07-04T16:57:02.027941Z","shell.execute_reply.started":"2024-07-04T16:57:01.999344Z","shell.execute_reply":"2024-07-04T16:57:02.026651Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"smartphone.columns","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:57:40.665343Z","iopub.execute_input":"2024-07-04T16:57:40.665755Z","iopub.status.idle":"2024-07-04T16:57:40.673502Z","shell.execute_reply.started":"2024-07-04T16:57:40.665722Z","shell.execute_reply":"2024-07-04T16:57:40.672364Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n# Create the scatter plot\nplt.figure(figsize=(10, 6))\nplt.scatter(smartphone['price'], smartphone['rating'], c='blue', alpha=0.7)\n\n# Add labels and title\nplt.xlabel('Price (USD)')\nplt.ylabel('Rating (out of 5)')\nplt.title('Price vs. User Rating')\n\n# Add grid lines\nplt.grid(True, linestyle='--', linewidth=0.5, color='gray', which='both', \n       axis='y')\n\n# Show the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:01:48.115555Z","iopub.execute_input":"2024-07-04T17:01:48.115991Z","iopub.status.idle":"2024-07-04T17:01:48.455432Z","shell.execute_reply.started":"2024-07-04T17:01:48.115954Z","shell.execute_reply":"2024-07-04T17:01:48.454045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create the bar chart\nplt.figure(figsize=(8, 6))\nplt.hist(smartphone['rating'], bins=10, edgecolor='black', alpha=0.7)\nplt.xlabel('Rating')\nplt.ylabel('Number of Phones')\nplt.title('Distribution of Smartphone Ratings')\nplt.xticks(bins)  # Set the x-axis tick labels to category labels\nplt.legend()\nplt.grid(axis='y', linestyle='--', linewidth=0.5, color='gray')\n\n# Show the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:04:54.897709Z","iopub.execute_input":"2024-07-04T17:04:54.898081Z","iopub.status.idle":"2024-07-04T17:04:55.379521Z","shell.execute_reply.started":"2024-07-04T17:04:54.898052Z","shell.execute_reply":"2024-07-04T17:04:55.378128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Define columns to create pie charts for\n# columns = ['brand_name', 'model','rating','processor_brand', 'num_cores', 'processor_speed',\n#        'battery_capacity', 'fast_charging_available', 'fast_charging',\n#        'ram_capacity', 'internal_memory', 'screen_size', 'refresh_rate',\n#        'num_rear_cameras', 'num_front_cameras', 'os', 'primary_camera_rear',\n#        'primary_camera_front', 'extended_memory_available', 'extended_upto',\n#        'resolution_width', 'resolution_height']\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Define columns to create pie charts for\ncolumns = ['brand_name' ,'num_cores', 'ram_capacity', 'refresh_rate','num_rear_cameras', 'num_front_cameras']  # Select a few for example\n\n# Number of rows and columns for the grid (adjust based on number of columns)\nrows = 3\ncols = 2\n\n# Loop through each column and create a subplot\nfig, axes = plt.subplots(rows, cols, figsize=(12, 10))  # Create a figure and subplots\n\n# Flatten the 2D array of axes for easier iteration\naxes = axes.ravel()\n\nfor col, ax in zip(columns, axes):\n  # Count occurrences of unique values\n  unique_values, counts = np.unique(smartphone[col], return_counts=True)\n\n  # Create the pie chart in the current subplot\n  ax.pie(counts, labels=unique_values, autopct=\"%1.1f%%\", startangle=140)\n  ax.axis('equal')  # Equal aspect ratio for a circular pie chart\n  ax.set_title(f\"Pie Chart for {col}\")\n\n# Adjust spacing and layout (optional)\nfig.tight_layout()\n\n# Show the grid of pie charts\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:11:58.941909Z","iopub.execute_input":"2024-07-04T17:11:58.942365Z","iopub.status.idle":"2024-07-04T17:12:00.275644Z","shell.execute_reply.started":"2024-07-04T17:11:58.942329Z","shell.execute_reply":"2024-07-04T17:12:00.274570Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create the scatter plot\nplt.figure(figsize=(10, 6))\nplt.scatter(smartphone['resolution_width'], smartphone['resolution_height'], c='blue', alpha=0.7)\n\n# Add labels and title\nplt.xlabel('Resolution Width (pixels)')\nplt.ylabel('Resolution Height (pixels)')\nplt.title('Trend in Primary Rear Camera Resolution')\n\n# Add grid lines\nplt.grid(True, linestyle='--', linewidth=0.5, color='gray', which='both', \n       axis='both')\n\n# Show the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:13:38.147375Z","iopub.execute_input":"2024-07-04T17:13:38.147781Z","iopub.status.idle":"2024-07-04T17:13:38.559717Z","shell.execute_reply.started":"2024-07-04T17:13:38.147747Z","shell.execute_reply":"2024-07-04T17:13:38.558468Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(8, 6))\nplt.hist(smartphone['price'], bins=10, edgecolor='black', alpha=0.7)  # Adjust bins as needed\nplt.xlabel('Price (USD)')\nplt.ylabel('Number of Phones')\nplt.title('Distribution of Smartphone Prices')\nplt.grid(axis='y', linestyle='--', linewidth=0.5, color='gray')\n\n# Show the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:15:11.265986Z","iopub.execute_input":"2024-07-04T17:15:11.266760Z","iopub.status.idle":"2024-07-04T17:15:11.614314Z","shell.execute_reply.started":"2024-07-04T17:15:11.266715Z","shell.execute_reply":"2024-07-04T17:15:11.613235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Create the scatter plot\nplt.figure(figsize=(10, 6))\nplt.scatter(smartphone['brand_name'], smartphone['price'], c='blue', alpha=0.7)\n\n# Add labels and title\nplt.xlabel('Brand')\nplt.ylabel('Price (USD)')\nplt.title('Price vs. Brand')\n\n# Rotate x-axis labels for better readability if there are many brands\nplt.xticks(rotation=90, ha='right')  # Optional\n\n# Add grid lines\nplt.grid(True, linestyle='--', linewidth=0.5, color='gray', which='both', \n       axis='y')\n\n# Show the plot\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:18:16.917936Z","iopub.execute_input":"2024-07-04T17:18:16.918974Z","iopub.status.idle":"2024-07-04T17:18:17.636157Z","shell.execute_reply.started":"2024-07-04T17:18:16.918933Z","shell.execute_reply":"2024-07-04T17:18:17.635031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"smartphone.columns","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:19:26.459624Z","iopub.execute_input":"2024-07-04T17:19:26.460150Z","iopub.status.idle":"2024-07-04T17:19:26.467477Z","shell.execute_reply.started":"2024-07-04T17:19:26.460115Z","shell.execute_reply":"2024-07-04T17:19:26.466301Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Define features and corresponding data lists\n# features = {'Number of Cores': num_cores,\n#             'Battery Capacity (mAh)': battery_capacity,\n#             'RAM Capacity (GB)': ram_capacity,\n#             'Screen Size (inches)': screen_size,\n#             'Number of Rear Cameras': num_rear_cameras}\n\n# # Create a figure for multiple boxplots\n# plt.figure(figsize=(12, 8))\n\n# # Loop through features and create boxplots\n# positions = range(len(features))  # Positions for each boxplot on the x-axis\n\n# for i, (feature_name, data) in enumerate(features.items()):\n#   plt.boxplot(data, positions=[positions[i]], notch=True, patch_artist=True, vert=True, \n#               showfliers=False, labels=[feature_name])  # Customize boxplot options\n\n# # Customize the plot\n# plt.xlabel('Features')\n# plt.ylabel('Values')\n# plt.xticks(positions, features.keys(), rotation=45, ha='right')  # Rotate x-axis labels\n# plt.title('Distribution of Smartphone Features')\n# plt.grid(True, linestyle='--', linewidth=0.5, color='gray', which='both', \n#        axis='y')\n# plt.tight_layout()  # Adjust spacing between subplots\n\n# # Show the plot\n# plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T17:20:03.762602Z","iopub.execute_input":"2024-07-04T17:20:03.763011Z","iopub.status.idle":"2024-07-04T17:20:03.803033Z","shell.execute_reply.started":"2024-07-04T17:20:03.762980Z","shell.execute_reply":"2024-07-04T17:20:03.801751Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Restaurant Dataset","metadata":{}},{"cell_type":"code","source":"restra.head()","metadata":{"execution":{"iopub.status.busy":"2024-07-04T16:57:02.029546Z","iopub.execute_input":"2024-07-04T16:57:02.029979Z","iopub.status.idle":"2024-07-04T16:57:02.056811Z","shell.execute_reply.started":"2024-07-04T16:57:02.029945Z","shell.execute_reply":"2024-07-04T16:57:02.055575Z"},"trusted":true},"execution_count":null,"outputs":[]}]}