LaptopPricing-ML.ipynb

{"cells":[{"cell_type":"markdown","id":"b4950f3c-32b6-444b-8a8c-b7e2f617a688","metadata":{},"source":["# Data Analysis and Machine Learning for Laptops Pricing\n"]},{"cell_type":"markdown","id":"308d642b-84a5-4876-b343-39f5775fe33e","metadata":{},"source":["### Importing Required Libraries"]},{"cell_type":"code","execution_count":1,"id":"ba69fbc5-d05f-4b2f-9aa8-27b5d5149afb","metadata":{},"outputs":[],"source":["from tqdm import tqdm\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","%matplotlib inline\n","from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV\n","from sklearn.linear_model import LinearRegression, Ridge\n","from sklearn.preprocessing import PolynomialFeatures"]},{"cell_type":"code","execution_count":2,"id":"10972d6f-47c1-405b-9f04-2cec25a5c0fe","metadata":{},"outputs":[],"source":["filepath = 'LaptopPricing-Dataset-3.csv'\n","df = pd.read_csv(filepath)"]},{"cell_type":"markdown","id":"e70ed276-cb44-44b3-b442-0e89fe0fe891","metadata":{},"source":["Print the value of df.head(). \n"]},{"cell_type":"code","execution_count":3,"id":"6acd2c57-b615-4673-88b6-934984c06c82","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["(238, 16)\n"]},{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0.1</th>\n","      <th>Unnamed: 0</th>\n","      <th>Manufacturer</th>\n","      <th>Category</th>\n","      <th>GPU</th>\n","      <th>OS</th>\n","      <th>CPU_core</th>\n","      <th>Screen_Size_inch</th>\n","      <th>CPU_frequency</th>\n","      <th>RAM_GB</th>\n","      <th>Storage_GB_SSD</th>\n","      <th>Weight_pounds</th>\n","      <th>Price</th>\n","      <th>Price-binned</th>\n","      <th>Screen-Full_HD</th>\n","      <th>Screen-IPS_panel</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>Acer</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>14.0</td>\n","      <td>0.551724</td>\n","      <td>8</td>\n","      <td>256</td>\n","      <td>3.52800</td>\n","      <td>978</td>\n","      <td>Low</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>Dell</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>3</td>\n","      <td>15.6</td>\n","      <td>0.689655</td>\n","      <td>4</td>\n","      <td>256</td>\n","      <td>4.85100</td>\n","      <td>634</td>\n","      <td>Low</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>2</td>\n","      <td>2</td>\n","      <td>Dell</td>\n","      <td>3</td>\n","      <td>1</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>15.6</td>\n","      <td>0.931034</td>\n","      <td>8</td>\n","      <td>256</td>\n","      <td>4.85100</td>\n","      <td>946</td>\n","      <td>Low</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>3</td>\n","      <td>3</td>\n","      <td>Dell</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>5</td>\n","      <td>13.3</td>\n","      <td>0.551724</td>\n","      <td>8</td>\n","      <td>128</td>\n","      <td>2.69010</td>\n","      <td>1244</td>\n","      <td>Low</td>\n","      <td>0</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>4</td>\n","      <td>4</td>\n","      <td>HP</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>1</td>\n","      <td>7</td>\n","      <td>15.6</td>\n","      <td>0.620690</td>\n","      <td>8</td>\n","      <td>256</td>\n","      <td>4.21155</td>\n","      <td>837</td>\n","      <td>Low</td>\n","      <td>1</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Unnamed: 0.1  Unnamed: 0 Manufacturer  Category  GPU  OS  CPU_core  \\\n","0             0           0         Acer         4    2   1         5   \n","1             1           1         Dell         3    1   1         3   \n","2             2           2         Dell         3    1   1         7   \n","3             3           3         Dell         4    2   1         5   \n","4             4           4           HP         4    2   1         7   \n","\n","   Screen_Size_inch  CPU_frequency  RAM_GB  Storage_GB_SSD  Weight_pounds  \\\n","0              14.0       0.551724       8             256        3.52800   \n","1              15.6       0.689655       4             256        4.85100   \n","2              15.6       0.931034       8             256        4.85100   \n","3              13.3       0.551724       8             128        2.69010   \n","4              15.6       0.620690       8             256        4.21155   \n","\n","   Price Price-binned  Screen-Full_HD  Screen-IPS_panel  \n","0    978          Low               0                 1  \n","1    634          Low               1                 0  \n","2    946          Low               1                 0  \n","3   1244          Low               0                 1  \n","4    837          Low               1                 0  "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["print(df.shape)\n","df.head()"]},{"cell_type":"markdown","id":"efd471eb-522d-4859-8f33-46a55e42bd32","metadata":{},"source":["Drop the two unnecessary columns that have been added into the file, 'Unnamed: 0' and 'Unnamed: 0.1'. Use drop to delete these columns.\n"]},{"cell_type":"code","execution_count":4,"id":"e8ae5ede-c1f4-4c1a-9804-f818fa5f34fb","metadata":{},"outputs":[],"source":["df.drop(['Unnamed: 0', 'Unnamed: 0.1'], axis=1, inplace=True)"]},{"cell_type":"markdown","id":"f7206abc-8304-4e15-a21a-303ec84c83ca","metadata":{},"source":["# Task 1 : Using Cross validation to improve the model\n","\n","Divide the dataset into x_data and y_data parameters. Here y_data is the \"Price\" attribute, and x_data has all other attributes in the data set.\n"]},{"cell_type":"code","execution_count":5,"id":"73925b96-abf9-4f44-8eb5-6349a6691259","metadata":{},"outputs":[],"source":["\n","y_data=df['Price']\n","x_data=df.drop('Price',axis=1)"]},{"cell_type":"markdown","id":"b6119c35-bc6f-4c5c-9e11-56781eb1cb14","metadata":{},"source":["Split the data set into training and testing subests such that you reserve 10% of the data set for testing purposes.\n"]},{"cell_type":"code","execution_count":6,"id":"bc133185-4a7f-48c2-99c7-1e509b71facd","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of train sample :  214\n","Number of test sample :  24\n"]}],"source":[" \n","x_train,x_test,y_train,y_test=train_test_split(x_data,y_data,test_size=0.1,random_state=1)\n","print(\"Number of train sample : \",x_train.shape[0])\n","print(\"Number of test sample : \",x_test.shape[0])"]},{"cell_type":"markdown","id":"350ce14c-59d3-4d7f-877b-a155eea85aca","metadata":{},"source":["Create a single variable linear regression model using \"CPU_frequency\" parameter. Print the R^2 value of this model for the training and testing subsets.\n"]},{"cell_type":"code","execution_count":7,"id":"2eea5e28-04e4-46e8-bc31-a1c7ea2fea25","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["-0.06599437350393789\n","0.14829792099817973\n"]}],"source":["\n","lm=LinearRegression()\n","lm.fit(x_train[['CPU_frequency']],y_train)\n","print(lm.score(x_test[['CPU_frequency']],y_test))\n","print(lm.score(x_train[['CPU_frequency']],y_train))"]},{"cell_type":"markdown","id":"3efadd61-0f96-44c7-a784-14adda4da77a","metadata":{},"source":["Run a 4-fold cross validation on the model and print the mean value of R^2 score along with its standard deviation.\n"]},{"cell_type":"code","execution_count":8,"id":"56e24dc9-b412-4fa0-bf7a-32e7a2950c93","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["The mean of the folds are  -0.16109232388595113  the standard deviation is  0.384957978666471\n"]}],"source":[" \n","scores=cross_val_score(lm,x_data[['CPU_frequency']],y_data,cv=4)\n","print(\"The mean of the folds are \",scores.mean(),\" the standard deviation is \",scores.std())\n"]},{"cell_type":"markdown","id":"305c622d-d081-4a8f-ad7d-6af0444a614c","metadata":{},"source":["# Task 2: Overfitting\n","\n","Split the data set into training and testing components again, this time reserving 50% of the data set for testing.\n"]},{"cell_type":"code","execution_count":9,"id":"cf519b7a-2be9-498d-9309-85279ca9a213","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of test sample :  (119, 13)\n","Number of train sample :  (119, 13)\n"]}],"source":["\n","x_train,x_test,y_train,y_test=train_test_split(x_data,y_data,test_size=0.5,random_state=1)\n","print(\"Number of test sample : \",x_test.shape)\n","print(\"Number of train sample : \",x_train.shape)"]},{"cell_type":"markdown","id":"0f56f23c-8dd4-4da6-af31-ccd8514eb7c1","metadata":{},"source":["To identify the point of overfitting the model on the parameter \"CPU_frequency\", you'll need to create polynomial features using the single attribute. \n","You need to evaluate the R^2 scores of the model created using different degrees of polynomial features, ranging from 1 to 5. Save this set of values of R^2 score as a list.\n"]},{"cell_type":"code","execution_count":10,"id":"1725dbbf-75e3-4f55-873f-98f4be8786dc","metadata":{},"outputs":[],"source":["\n","lre=LinearRegression()\n","Rscore=[]\n","order=[1,2,3,4,5]\n","for n in order:\n","    pr=PolynomialFeatures(degree=n)\n","    x_train_pr=pr.fit_transform(x_train[['CPU_frequency']])\n","    x_test_pr=pr.fit_transform(x_test[['CPU_frequency']])\n","    lre.fit(x_train_pr,y_train)\n","    Rscore.append(lre.score(x_test_pr,y_train))"]},{"cell_type":"markdown","id":"037b2089-8443-4282-b8b8-48b2dab501d7","metadata":{},"source":["Plot the values of R^2 scores against the order. Note the point where the score drops.\n"]},{"cell_type":"code","execution_count":11,"id":"1f32a49c-2c9d-4264-8924-725dac69c1bc","metadata":{},"outputs":[{"data":{"text/plain":["Text(0.5, 1.0, 'R^2 Using Test Data')"]},"execution_count":11,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","plt.plot(order,Rscore)\n","plt.xlabel('order')\n","plt.ylabel('R^2')\n","plt.title('R^2 Using Test Data')"]},{"cell_type":"markdown","id":"2c31e63e-30ee-4ab8-924f-e15902685d3d","metadata":{},"source":["# Task 3 : Ridge Regression\n","\n","Now consider that you have multiple features, i.e. 'CPU_frequency', 'RAM_GB', 'Storage_GB_SSD', 'CPU_core','OS','GPU' and 'Category'. Create a polynomial feature model that uses all these parameters with degree=2. Also create the training and testing attribute sets.\n"]},{"cell_type":"code","execution_count":12,"id":"a4f63b20-2350-4099-ba77-db15efe09499","metadata":{},"outputs":[],"source":["\n","pr=PolynomialFeatures(degree=2)\n","x_train_pr=pr.fit_transform(x_train[['CPU_frequency','RAM_GB','Storage_GB_SSD','CPU_core','OS','GPU','Category']])\n","x_test_pr=pr.fit_transform(x_test[['CPU_frequency','RAM_GB','Storage_GB_SSD','CPU_core','OS','GPU','Category']])\n"]},{"cell_type":"markdown","id":"c9bb1e28-4e11-48be-833f-d49a57e8e590","metadata":{},"source":["Create a Ridge Regression model and evaluate it using values of the hyperparameter alpha ranging from 0.001 to 1 with increments of 0.001. \n","Create a list of all Ridge Regression R^2 scores for training and testing data. \n"]},{"cell_type":"code","execution_count":13,"id":"2ea428c9-40d4-4a25-b52c-c6133f43c904","metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["100%|██████████| 999/999 [00:09<00:00, 105.88it/s, Test Score=0.507, Train Score=0.642]\n"]}],"source":["\n","Rsqu_test=[]\n","Rsqu_train=[]\n","Alpha=np.arange(0.001,1,0.001)\n","pbar=tqdm(Alpha)\n","for alpha in pbar:\n","    RidgeModel=Ridge(alpha=alpha)\n","    RidgeModel.fit(x_train_pr,y_train)\n","    test_score,train_score=RidgeModel.score(x_test_pr,y_test),RidgeModel.score(x_train_pr,y_train)\n","    pbar.set_postfix({\"Test Score\":test_score,\"Train Score\":train_score})\n","    Rsqu_test.append(test_score)\n","    Rsqu_train.append(train_score)"]},{"cell_type":"markdown","id":"2ac05e26-7bf8-4e5e-b603-9a32b9059c46","metadata":{},"source":["Plot the R^2 values for training and testing sets with respect to the value of alpha\n"]},{"cell_type":"code","execution_count":14,"id":"d8a56582-ec8b-4019-822c-a926ee365796","metadata":{},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x1e60541d3d0>"]},"execution_count":14,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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size 1000x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["\n","width=10\n","height=6\n","plt.figure(figsize=(width,height))\n","plt.plot(Alpha,Rsqu_test,label='Validation Data')\n","plt.plot(Alpha,Rsqu_train,'r',label='Training Data')\n","plt.xlabel('Alpha')\n","plt.ylabel('R^2 ')\n","plt.ylim(0,1)\n","plt.legend()"]},{"cell_type":"markdown","id":"3be89f95-6954-4240-b046-c531a36b6187","metadata":{},"source":["# Task 4: Grid Search\n","\n","Using the raw data and the same set of features as used above, use GridSearchCV to identify the value of alpha for which the model performs best. \n","Assume the set of alpha values to be used as\n","```math\n","{0.0001, 0.001, 0.01, 0.1, 1, 10}\n","```\n"]},{"cell_type":"code","execution_count":15,"id":"54864630-2857-4fee-9fb2-342d604f6e09","metadata":{},"outputs":[],"source":["\n","parameters= [{'alpha':[0.0001,0.001,0.01,0.1,1,10]}]\n"]},{"cell_type":"markdown","id":"3d133452-8683-4d27-9d42-ea8ce0fca4f0","metadata":{},"source":["Create a Ridge instance and run Grid Search using a 4 fold cross validation.\n"]},{"cell_type":"code","execution_count":16,"id":"b3f09f1f-5629-4f71-80c0-68162de41abf","metadata":{},"outputs":[{"data":{"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: 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relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=4, estimator=Ridge(),\n","             param_grid=[{&#x27;alpha&#x27;: [0.0001, 0.001, 0.01, 0.1, 1, 10]}])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=4, estimator=Ridge(),\n","             param_grid=[{&#x27;alpha&#x27;: [0.0001, 0.001, 0.01, 0.1, 1, 10]}])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div>"],"text/plain":["GridSearchCV(cv=4, estimator=Ridge(),\n","             param_grid=[{'alpha': [0.0001, 0.001, 0.01, 0.1, 1, 10]}])"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["\n","RR=Ridge()\n","Grid1=GridSearchCV(RR,parameters,cv=4)\n","Grid1.fit(x_train[['CPU_frequency','RAM_GB','Storage_GB_SSD','CPU_core','OS','GPU','Category']],y_train)\n","\n"]},{"cell_type":"markdown","id":"dc61b9f7-cf8f-42e8-b781-d2e2d0dff616","metadata":{},"source":["Print the R^2 score for the test data using the estimator that uses the derived optimum value of alpha.\n"]},{"cell_type":"code","execution_count":17,"id":"84e45f3a-d4be-400d-bd28-d22b7235a218","metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["0.42296349452687465\n"]}],"source":["\n","BestRR=Grid1.best_estimator_\n","print(BestRR.score(x_test[['CPU_frequency','RAM_GB','Storage_GB_SSD','CPU_core','OS','GPU','Category']],y_test))\n"]}],"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":"3.11.7"}},"nbformat":4,"nbformat_minor":4}