Interview Preparation- Day 2- Linear Regression.ipynb

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   "source": [
    "### How To Learn Machine Learning Algorithms For Interviews\n",
    "\n",
    "#### Linear Regression\n",
    "\n",
    "Theoretical Understanding:\n",
    "\n",
    "1. https://www.youtube.com/watch?v=1-OGRohmH2s&list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&index=29\n",
    "2. https://www.youtube.com/watch?v=5rvnlZWzox8&list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&index=34\n",
    "3. https://www.youtube.com/watch?v=NAPhUDjgG_s&list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&index=32\n",
    "4. https://www.youtube.com/watch?v=WuuyD3Yr-js&list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&index=35\n",
    "5. https://www.youtube.com/watch?v=BqzgUnrNhFM&list=PLZoTAELRMXVPBTrWtJkn3wWQxZkmTXGwe&index=33"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Interview Question on Multicollinearity\n",
    "\n",
    "1. https://www.youtube.com/watch?v=tcaruVHXZwE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 1. What Are the Basic Assumption?(favourite)\n",
    "There are four assumptions associated with a linear regression model:\n",
    "\n",
    "1. Linearity: The relationship between X and the mean of Y is linear.\n",
    "2. Homoscedasticity: The variance of residual is the same for any value of X.\n",
    "3. Independence: Observations are independent of each other.\n",
    "4. Normality: For any fixed value of X, Y is normally distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  2. Advantages\n",
    "1. Linear regression performs exceptionally well for linearly separable data\n",
    "2. Easy to implement and train the model\n",
    "3. It can handle overfitting using dimensionlity reduction techniques and cross validation and regularization \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 3. Disadvantages\n",
    "1. Sometimes Lot of Feature Engineering Is required\n",
    "2. If the independent features are correlated it may affect performance\n",
    "3. It is often quite prone to noise and overfitting"
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 4. Whether Feature Scaling is required?\n",
    "Yes\n",
    "##### 5. Impact of Missing Values?\n",
    "It is sensitive to missing values\n",
    "##### 6. Impact of outliers?\n",
    "linear regression needs the relationship between the independent and dependent variables to be linear. It is also important to check for outliers since linear regression is sensitive to outlier effects.\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Types of Problems it can solve(Supervised)\n",
    "1. Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Overfitting And Underfitting\n",
    "HomeWork?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Different Problem statement you can solve using Linear Regression\n",
    "1. Advance House Price Prediction\n",
    "2. Flight Price Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Practical Implementation\n",
    "1. https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}