scikit-learn.ipynb

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   "source": [
    "## **Scikit-learn**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scikit-learn is a popular machine learning library in Python. It provides simple and efficient tools for data mining, data analysis, and machine learning. It supports both supervised and unsupervised learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic Workflow"
   ]
  },
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   "source": [
    "##### import Scikit-learn and iris"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Loading Data"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris = load_iris()\n",
    "X, y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Splitting Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Training a Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# Initialize the model\n",
    "model = DecisionTreeClassifier()\n",
    "\n",
    "# Train the model\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Making Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on test data\n",
    "predictions = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Evaluate accuracy\n",
    "accuracy = accuracy_score(y_test, predictions)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  }
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