{"id":27311705,"url":"https://github.com/mkhekare/bikesharing_ml","last_synced_at":"2026-04-20T10:33:03.504Z","repository":{"id":286881351,"uuid":"962865792","full_name":"mkhekare/bikesharing_ml","owner":"mkhekare","description":"Provides insights into the bike sharing trends based on various factors such as time of day, season, and weather. 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The focus is on the hourly data file, `bikes.csv`.\n\n## Reference\nFanaee-T, Hadi, and Gama, Joao, \"Event labeling combining ensemble detectors and background knowledge\", Progress in Artificial Intelligence (2013): pp. 1-15, Springer Berlin Heidelberg.\n\n## Data Manipulation\n\n```python\nimport numpy as np\nimport pandas as pd\nimport seaborn as sn\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\n# Setting parameters for visualization\nparams = {\n    'legend.fontsize': 'x-large',\n    'figure.figsize': (30, 10),\n    'axes.labelsize': 'x-large',\n    'axes.titlesize': 'x-large',\n    'xtick.labelsize': 'x-large',\n    'ytick.labelsize': 'x-large'\n}\n\nsn.set_style('whitegrid')\nsn.set_context('talk')\nplt.rcParams.update(params)\npd.options.display.max_colwidth = 600\n\n# Load Dataset\nhour_df = pd.read_csv('bikes.csv')\nprint(\"Shape of dataset::{}\".format(hour_df.shape))\n```\n\n### Dataset Overview\nThe dataset contains 17 attributes and over 17,000 records. The attributes include:\n\n- `instant`: Record ID\n- `dteday`: Date\n- `season`: Season (1: winter, 2: spring, 3: summer, 4: fall)\n- `yr`: Year (0: 2011, 1: 2012)\n- `mnth`: Month\n- `hr`: Hour\n- `holiday`: Whether the day is a holiday\n- `weekday`: Day of the week\n- `workingday`: Whether the day is a working day\n- `weathersit`: Weather situation\n- `temp`: Normalized temperature\n- `atemp`: Normalized feeling temperature\n- `hum`: Normalized humidity\n- `windspeed`: Normalized wind speed\n- `casual`: Count of casual users\n- `registered`: Count of registered users\n- `cnt`: Total count of users\n\n### Data Types and Summary Stats\n```python\n# Data types of attributes\nhour_df.dtypes\n```\n\n### Standardize Attribute Names\n```python\nhour_df.rename(columns={\n    'instant': 'rec_id',\n    'dteday': 'datetime',\n    'holiday': 'is_holiday',\n    'workingday': 'is_workingday',\n    'weathersit': 'weather_condition',\n    'hum': 'humidity',\n    'mnth': 'month',\n    'cnt': 'total_count',\n    'hr': 'hour',\n    'yr': 'year'\n}, inplace=True)\n```\n\n### Typecast Attributes\n```python\n# Date time conversion\nhour_df['datetime'] = pd.to_datetime(hour_df.datetime)\n\n# Categorical variables\ncategorical_columns = ['season', 'is_holiday', 'weekday', 'weather_condition', 'is_workingday', 'month', 'year', 'hour']\nfor col in categorical_columns:\n    hour_df[col] = hour_df[col].astype('category')\n```\n\n## Visualize Attributes, Trends, and Relationships\n\n### Hourly Distribution of Total Counts\n```python\nfig, ax = plt.subplots()\nsn.lineplot(data=hour_df, x='hour', y='total_count', hue='season', ax=ax)\nax.set(title=\"Season-wise hourly distribution of counts\")\nplt.show()\n```\n\n### Monthly Distribution of Total Counts\n```python\nfig, ax = plt.subplots()\nsn.barplot(data=hour_df[['month', 'total_count']], x=\"month\", y=\"total_count\")\nax.set(title=\"Monthly distribution of counts\")\nplt.show()\n```\n\n### Correlation Analysis\n```python\ncorrMatt = hour_df[[\"temp\", \"atemp\", \"humidity\", \"windspeed\", \"casual\", \"registered\", \"total_count\"]].corr()\nmask = np.array(corrMatt)\nmask[np.tril_indices_from(mask)] = False\nsn.heatmap(corrMatt, mask=mask, vmax=.8, square=True, annot=True)\nplt.show()\n```\n\n## Conclusion\nThis analysis provides insights into the bike sharing trends based on various factors such as time of day, season, and weather. The findings can be used to improve bike sharing services and understand user behavior.\n\n## Dependencies\n- numpy\n- pandas\n- seaborn\n- matplotlib\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkhekare%2Fbikesharing_ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmkhekare%2Fbikesharing_ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmkhekare%2Fbikesharing_ml/lists"}