Assignment 6.ipynb

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    "# Assignment 6a: Building a Weather Monitoring System\n",
    "\n",
    "## Objective:\n",
    "To design and implement a Weather Monitoring System using Object-Oriented Programming principles in Python. The system should allow the management of weather stations, recording weather data, and generating reports.\n",
    "\n",
    "## Requirements:\n",
    "\n",
    "### Class Design:\n",
    "- `WeatherStation`: Manages weather data and generates reports.\n",
    "- `WeatherData`: Represents weather data with attributes like temperature, humidity, wind speed, and date.\n",
    "- `WeatherReport`: Represents a weather report with summary statistics.\n",
    "- `Location`: Represents the location of a weather station with attributes like city, state, and coordinates.\n",
    "\n",
    "### Attributes and Methods:\n",
    "\n",
    "#### Class `WeatherStation`:\n",
    "- **Attributes**:\n",
    "  - `location`: Instance of `Location` representing the station's location.\n",
    "  - `weather_records`: List of `WeatherData` objects.\n",
    "- **Methods**:\n",
    "  - `add_weather_data(weather_data)`: Adds a `WeatherData` record to the station.\n",
    "  - `remove_weather_data(weather_data)`: Removes a `WeatherData` record from the station.\n",
    "  - `generate_report(start_date, end_date)`: Generates a `WeatherReport` for the specified date range.\n",
    "  - `__str__()`: Returns a string representation of the weather station.\n",
    "\n",
    "#### Class `WeatherData`:\n",
    "- **Attributes**:\n",
    "  - `temperature`: Temperature recorded.\n",
    "  - `humidity`: Humidity level recorded.\n",
    "  - `wind_speed`: Wind speed recorded.\n",
    "  - `date`: Date of the weather data.\n",
    "- **Methods**:\n",
    "  - `__str__()`: Returns a string representation of the weather data.\n",
    "\n",
    "#### Class `WeatherReport`:\n",
    "- **Attributes**:\n",
    "  - `average_temperature`: Average temperature over the period.\n",
    "  - `average_humidity`: Average humidity over the period.\n",
    "  - `average_wind_speed`: Average wind speed over the period.\n",
    "  - `total_days`: Total number of days in the report.\n",
    "- **Methods**:\n",
    "  - `__str__()`: Returns a string representation of the weather report.\n",
    "\n",
    "#### Class `Location`:\n",
    "- **Attributes**:\n",
    "  - `city`: City where the weather station is located.\n",
    "  - `state`: State where the weather station is located.\n",
    "  - `coordinates`: Tuple representing latitude and longitude.\n",
    "- **Methods**:\n",
    "  - `__str__()`: Returns a string representation of the location.\n",
    "\n",
    "## Instructions:\n",
    "\n",
    "### Setup:\n",
    "- Create a new Python file for this assignment.\n",
    "- Define the classes and their methods as described above.\n",
    "\n",
    "### Implementation:\n",
    "- Implement the `WeatherStation` class to manage weather data and generate reports.\n",
    "- Implement the `WeatherData` class to represent individual weather records.\n",
    "- Implement the `WeatherReport` class to summarize weather data over a period.\n",
    "- Implement the `Location` class to represent the location of a weather station.\n",
    "\n",
    "### Functionality:\n",
    "- Ensure that the system can add and remove weather data records.\n",
    "- Allow the generation of weather reports for specified date ranges.\n",
    "- Provide functionality to display summary statistics in the weather reports.\n",
    "\n",
    "### Testing:\n",
    "- Write test cases to verify the functionality of each class and method.\n",
    "- Test adding and removing weather data records.\n",
    "- Test generating weather reports for different date ranges.\n",
    "- Ensure the system handles edge cases, such as generating reports with no data.\n",
    "\n",
    "## Example Usage:\n",
    "\n",
    "```python\n",
    "# Create a location\n",
    "location = Location(\"San Francisco\", \"CA\", (37.7749, -122.4194))\n",
    "\n",
    "# Create a weather station\n",
    "station = WeatherStation(location)\n",
    "\n",
    "# Add weather data\n",
    "data1 = WeatherData(68, 55, 15, \"2024-06-01\")\n",
    "data2 = WeatherData(70, 60, 10, \"2024-06-02\")\n",
    "data3 = WeatherData(65, 58, 20, \"2024-06-03\")\n",
    "station.add_weather_data(data1)\n",
    "station.add_weather_data(data2)\n",
    "station.add_weather_data(data3)\n",
    "\n",
    "# Generate a report\n",
    "report = station.generate_report(\"2024-06-01\", \"2024-06-03\")\n",
    "\n",
    "# Print the report\n",
    "print(report)\n",
    "# Remove weather data\n",
    "station.remove_weather_data(data1)\n",
    "\n",
    "# Print the updated station data\n",
    "print(station)\n"
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    "# Assignment 6b: Implementing Logistic Regression from Scratch\n",
    "\n",
    "## Objective:\n",
    "To design and implement a logistic regression model from scratch using NumPy, functions, and classes. This will help in understanding the foundational concepts needed for neural networks.\n",
    "\n",
    "## Requirements:\n",
    "\n",
    "### Class Design:\n",
    "- `LogisticRegression`: Implements logistic regression.\n",
    "- The class should handle the fitting, predicting, and evaluation of the model.\n",
    "\n",
    "### Attributes and Methods:\n",
    "\n",
    "#### Class `LogisticRegression`:\n",
    "- **Attributes**:\n",
    "  - `weights`: Weights of the logistic model.\n",
    "- **Methods**:\n",
    "  - `fit(X, y, epochs, lr)`: Fits the logistic model to the data using gradient descent.\n",
    "  - `predict(X)`: Predicts probabilities for given input data.\n",
    "  - `predict_classes(X)`: Predicts class labels for given input data.\n",
    "  - `evaluate(X, y)`: Evaluates the model using accuracy.\n",
    "  - `sigmoid(z)`: Computes the sigmoid function.\n",
    "\n",
    "## Instructions:\n",
    "\n",
    "### Setup:\n",
    "- Create a new Python file for this assignment.\n",
    "- Define the class and its methods as described above.\n",
    "- Use NumPy for numerical computations.\n",
    "\n",
    "### Implementation:\n",
    "- Implement the `LogisticRegression` class to handle binary classification.\n",
    "- Ensure that the model can fit the data, make predictions, and evaluate performance.\n",
    "\n",
    "### Functionality:\n",
    "- The `LogisticRegression` class should include a method for gradient descent optimization.\n",
    "- Include methods for evaluating model performance using accuracy.\n",
    "\n",
    "### Testing:\n",
    "- Write test cases to verify the functionality of the class and methods.\n",
    "- Test the logistic regression model with binary classification data.\n",
    "- Ensure the system handles edge cases, such as poorly conditioned data.\n",
    "\n",
    "## Example Usage:\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "\n",
    "# Logistic Regression Example\n",
    "X_log = np.array([[0.5], [1.5], [2.5], [3.5], [4.5]])\n",
    "y_log = np.array([0, 0, 1, 1, 1])\n",
    "\n",
    "logistic_model = LogisticRegression()\n",
    "logistic_model.fit(X_log, y_log, epochs=1000, lr=0.01)\n",
    "log_predictions = logistic_model.predict(X_log)\n",
    "log_classes = logistic_model.predict_classes(X_log)\n",
    "accuracy = logistic_model.evaluate(X_log, y_log)\n",
    "print(\"Logistic Regression Accuracy:\", accuracy)"
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