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                "# ML Practice Series: Module 18 - Time Series Analysis\n",
                "\n",
                "Welcome to Module 18! **Time Series Analysis** is the study of data points collected or recorded at specific time intervals. This is crucial for finance, weather forecasting, and inventory management.\n",
                "\n",
                "### Objectives:\n",
                "1. **Datetime Handling**: Converting strings to date objects.\n",
                "2. **Resampling & Rolling Windows**: Smoothing data trends.\n",
                "3. **Stationarity**: Understanding the Mean and Variance over time.\n",
                "4. **Forecasting**: A simple look at the Moving Average model.\n",
                "\n",
                "---"
            ]
        },
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            "source": [
                "## 1. Setup\n",
                "We will use the **Air Passengers** dataset, which shows monthly totals of international airline passengers from 1949 to 1960."
            ]
        },
        {
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            "source": [
                "import pandas as pd\n",
                "import numpy as np\n",
                "import matplotlib.pyplot as plt\n",
                "import seaborn as sns\n",
                "\n",
                "# Load dataset\n",
                "url = \"https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv\"\n",
                "df = pd.read_csv(url, parse_dates=['Month'], index_index=True)\n",
                "\n",
                "print(\"Dataset head:\")\n",
                "print(df.head())\n",
                "\n",
                "plt.figure(figsize=(12, 6))\n",
                "plt.plot(df)\n",
                "plt.title('Monthly International Airline Passengers')\n",
                "plt.show()"
            ]
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                "## 2. Feature Extraction from Time\n",
                "\n",
                "### Task 1: Component Extraction\n",
                "Extract the `Year`, `Month`, and `Day of Week` from the index into new columns.\n",
                "\n",
                "*Web Reference: [Feature Engineering Guide](https://aashishgarg13.github.io/DataScience/feature-engineering/) (Time features section).*"
            ]
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            "source": [
                "# YOUR CODE HERE\n"
            ]
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            "source": [
                "<details>\n",
                "<summary><b>Click to see Solution</b></summary>\n",
                "\n",
                "```python\n",
                "df['year'] = df.index.year\n",
                "df['month'] = df.index.month\n",
                "df['day_of_week'] = df.index.dayofweek\n",
                "df.head()\n",
                "```\n",
                "</details>"
            ]
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            "metadata": {},
            "source": [
                "## 3. Smoothing Trends\n",
                "\n",
                "### Task 2: Rolling Mean\n",
                "Calculate and plot a 12-month rolling mean to see the yearly trend more clearly."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "# YOUR CODE HERE\n"
            ]
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            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "<details>\n",
                "<summary><b>Click to see Solution</b></summary>\n",
                "\n",
                "```python\n",
                "rolling_mean = df['Passengers'].rolling(window=12).mean()\n",
                "\n",
                "plt.figure(figsize=(12, 6))\n",
                "plt.plot(df['Passengers'], label='Original')\n",
                "plt.plot(rolling_mean, color='red', label='12-Month Rolling Mean')\n",
                "plt.legend()\n",
                "plt.show()\n",
                "```\n",
                "</details>"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "--- \n",
                "### Excellent Forecast! \n",
                "Time Series is a deep field. You've now mastered the basics of handling temporal data.\n",
                "Next: **Natural Language Processing (NLP)**."
            ]
        }
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