multiple_linear_regression(done).ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1lEahf6t2ADV8u3yRrxPYQj1QVNknvxl6","timestamp":1730420036280}]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"CazISR8X_HUG"},"source":["# Multiple Linear Regression"]},{"cell_type":"markdown","metadata":{"id":"pOyqYHTk_Q57"},"source":["## Importing the libraries"]},{"cell_type":"code","source":["import numpy as np\n","import matplotlib.pyplot as plt\n","import pandas as pd"],"metadata":{"id":"kEy30bCTGxJd","executionInfo":{"status":"ok","timestamp":1730421511237,"user_tz":-420,"elapsed":7,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"vgC61-ah_WIz"},"source":["## Importing the dataset"]},{"cell_type":"code","source":["dataset = pd.read_csv('50_Startups.csv')\n","print(dataset)\n","x = dataset.iloc[:,:-1].values\n","y = dataset.iloc[:,-1].values"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QwFYaTQGG3qE","executionInfo":{"status":"ok","timestamp":1730421674829,"user_tz":-420,"elapsed":525,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"23a7f683-6f1b-4a8a-c243-3636ef51c762"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stdout","text":["    R&D Spend  Administration  Marketing Spend       State     Profit\n","0   165349.20       136897.80        471784.10    New York  192261.83\n","1   162597.70       151377.59        443898.53  California  191792.06\n","2   153441.51       101145.55        407934.54     Florida  191050.39\n","3   144372.41       118671.85        383199.62    New York  182901.99\n","4   142107.34        91391.77        366168.42     Florida  166187.94\n","5   131876.90        99814.71        362861.36    New York  156991.12\n","6   134615.46       147198.87        127716.82  California  156122.51\n","7   130298.13       145530.06        323876.68     Florida  155752.60\n","8   120542.52       148718.95        311613.29    New York  152211.77\n","9   123334.88       108679.17        304981.62  California  149759.96\n","10  101913.08       110594.11        229160.95     Florida  146121.95\n","11  100671.96        91790.61        249744.55  California  144259.40\n","12   93863.75       127320.38        249839.44     Florida  141585.52\n","13   91992.39       135495.07        252664.93  California  134307.35\n","14  119943.24       156547.42        256512.92     Florida  132602.65\n","15  114523.61       122616.84        261776.23    New York  129917.04\n","16   78013.11       121597.55        264346.06  California  126992.93\n","17   94657.16       145077.58        282574.31    New York  125370.37\n","18   91749.16       114175.79        294919.57     Florida  124266.90\n","19   86419.70       153514.11             0.00    New York  122776.86\n","20   76253.86       113867.30        298664.47  California  118474.03\n","21   78389.47       153773.43        299737.29    New York  111313.02\n","22   73994.56       122782.75        303319.26     Florida  110352.25\n","23   67532.53       105751.03        304768.73     Florida  108733.99\n","24   77044.01        99281.34        140574.81    New York  108552.04\n","25   64664.71       139553.16        137962.62  California  107404.34\n","26   75328.87       144135.98        134050.07     Florida  105733.54\n","27   72107.60       127864.55        353183.81    New York  105008.31\n","28   66051.52       182645.56        118148.20     Florida  103282.38\n","29   65605.48       153032.06        107138.38    New York  101004.64\n","30   61994.48       115641.28         91131.24     Florida   99937.59\n","31   61136.38       152701.92         88218.23    New York   97483.56\n","32   63408.86       129219.61         46085.25  California   97427.84\n","33   55493.95       103057.49        214634.81     Florida   96778.92\n","34   46426.07       157693.92        210797.67  California   96712.80\n","35   46014.02        85047.44        205517.64    New York   96479.51\n","36   28663.76       127056.21        201126.82     Florida   90708.19\n","37   44069.95        51283.14        197029.42  California   89949.14\n","38   20229.59        65947.93        185265.10    New York   81229.06\n","39   38558.51        82982.09        174999.30  California   81005.76\n","40   28754.33       118546.05        172795.67  California   78239.91\n","41   27892.92        84710.77        164470.71     Florida   77798.83\n","42   23640.93        96189.63        148001.11  California   71498.49\n","43   15505.73       127382.30         35534.17    New York   69758.98\n","44   22177.74       154806.14         28334.72  California   65200.33\n","45    1000.23       124153.04          1903.93    New York   64926.08\n","46    1315.46       115816.21        297114.46     Florida   49490.75\n","47       0.00       135426.92             0.00  California   42559.73\n","48     542.05        51743.15             0.00    New York   35673.41\n","49       0.00       116983.80         45173.06  California   14681.40\n"]}]},{"cell_type":"markdown","metadata":{"id":"VadrvE7s_lS9"},"source":["## Encoding categorical data"]},{"cell_type":"code","source":["from sklearn.compose import ColumnTransformer\n","from sklearn.preprocessing import OneHotEncoder\n","ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [3])], remainder='passthrough')\n","x = np.array(ct.fit_transform(x))"],"metadata":{"id":"wdhqNkZaHLdp","executionInfo":{"status":"ok","timestamp":1730421572716,"user_tz":-420,"elapsed":530,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"WemVnqgeA70k"},"source":["## Splitting the dataset into the Training set and Test set"]},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","x_train , x_test, y_train,y_test = train_test_split(x,y,test_size = 0.3,random_state= 42)"],"metadata":{"id":"9PZn2CcfIItE","executionInfo":{"status":"ok","timestamp":1730421616221,"user_tz":-420,"elapsed":525,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}}},"execution_count":13,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"k-McZVsQBINc"},"source":["## Training the Multiple Linear Regression model on the Training set"]},{"cell_type":"code","source":["from sklearn.linear_model import LinearRegression\n","regressor = LinearRegression()\n","regressor.fit(x_train,y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":80},"id":"vryWdNolIJzj","executionInfo":{"status":"ok","timestamp":1730421620257,"user_tz":-420,"elapsed":546,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"768fdc57-088b-4af3-a130-694b9d7bdb2f"},"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["LinearRegression()"],"text/html":["<style>#sk-container-id-1 {\n","  /* Definition of color scheme common for light and dark mode */\n","  --sklearn-color-text: black;\n","  --sklearn-color-line: gray;\n","  /* Definition of color scheme for unfitted estimators */\n","  --sklearn-color-unfitted-level-0: #fff5e6;\n","  --sklearn-color-unfitted-level-1: #f6e4d2;\n","  --sklearn-color-unfitted-level-2: #ffe0b3;\n","  --sklearn-color-unfitted-level-3: chocolate;\n","  /* Definition of color scheme for fitted estimators */\n","  --sklearn-color-fitted-level-0: #f0f8ff;\n","  --sklearn-color-fitted-level-1: #d4ebff;\n","  --sklearn-color-fitted-level-2: #b3dbfd;\n","  --sklearn-color-fitted-level-3: cornflowerblue;\n","\n","  /* Specific color for light theme */\n","  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n","  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n","  --sklearn-color-icon: #696969;\n","\n","  @media (prefers-color-scheme: dark) {\n","    /* Redefinition of color scheme for dark theme */\n","    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n","    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n","    --sklearn-color-icon: #878787;\n","  }\n","}\n","\n","#sk-container-id-1 {\n","  color: var(--sklearn-color-text);\n","}\n","\n","#sk-container-id-1 pre {\n","  padding: 0;\n","}\n","\n","#sk-container-id-1 input.sk-hidden--visually {\n","  border: 0;\n","  clip: rect(1px 1px 1px 1px);\n","  clip: rect(1px, 1px, 1px, 1px);\n","  height: 1px;\n","  margin: -1px;\n","  overflow: hidden;\n","  padding: 0;\n","  position: absolute;\n","  width: 1px;\n","}\n","\n","#sk-container-id-1 div.sk-dashed-wrapped {\n","  border: 1px dashed var(--sklearn-color-line);\n","  margin: 0 0.4em 0.5em 0.4em;\n","  box-sizing: border-box;\n","  padding-bottom: 0.4em;\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","#sk-container-id-1 div.sk-container {\n","  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n","     but bootstrap.min.css set `[hidden] { display: none !important; }`\n","     so we also need the `!important` here to be able to override the\n","     default hidden behavior on the sphinx rendered scikit-learn.org.\n","     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n","  display: inline-block !important;\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-text-repr-fallback {\n","  display: none;\n","}\n","\n","div.sk-parallel-item,\n","div.sk-serial,\n","div.sk-item {\n","  /* draw centered vertical line to link estimators */\n","  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n","  background-size: 2px 100%;\n","  background-repeat: no-repeat;\n","  background-position: center center;\n","}\n","\n","/* Parallel-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-parallel-item::after {\n","  content: \"\";\n","  width: 100%;\n","  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n","  flex-grow: 1;\n","}\n","\n","#sk-container-id-1 div.sk-parallel {\n","  display: flex;\n","  align-items: stretch;\n","  justify-content: center;\n","  background-color: var(--sklearn-color-background);\n","  position: relative;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item {\n","  display: flex;\n","  flex-direction: column;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:first-child::after {\n","  align-self: flex-end;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:last-child::after {\n","  align-self: flex-start;\n","  width: 50%;\n","}\n","\n","#sk-container-id-1 div.sk-parallel-item:only-child::after {\n","  width: 0;\n","}\n","\n","/* Serial-specific style estimator block */\n","\n","#sk-container-id-1 div.sk-serial {\n","  display: flex;\n","  flex-direction: column;\n","  align-items: center;\n","  background-color: var(--sklearn-color-background);\n","  padding-right: 1em;\n","  padding-left: 1em;\n","}\n","\n","\n","/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n","clickable and can be expanded/collapsed.\n","- Pipeline and ColumnTransformer use this feature and define the default style\n","- Estimators will overwrite some part of the style using the `sk-estimator` class\n","*/\n","\n","/* Pipeline and ColumnTransformer style (default) */\n","\n","#sk-container-id-1 div.sk-toggleable {\n","  /* Default theme specific background. It is overwritten whether we have a\n","  specific estimator or a Pipeline/ColumnTransformer */\n","  background-color: var(--sklearn-color-background);\n","}\n","\n","/* Toggleable label */\n","#sk-container-id-1 label.sk-toggleable__label {\n","  cursor: pointer;\n","  display: block;\n","  width: 100%;\n","  margin-bottom: 0;\n","  padding: 0.5em;\n","  box-sizing: border-box;\n","  text-align: center;\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n","  /* Arrow on the left of the label */\n","  content: \"▸\";\n","  float: left;\n","  margin-right: 0.25em;\n","  color: var(--sklearn-color-icon);\n","}\n","\n","#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n","  color: var(--sklearn-color-text);\n","}\n","\n","/* Toggleable content - dropdown */\n","\n","#sk-container-id-1 div.sk-toggleable__content {\n","  max-height: 0;\n","  max-width: 0;\n","  overflow: hidden;\n","  text-align: left;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content pre {\n","  margin: 0.2em;\n","  border-radius: 0.25em;\n","  color: var(--sklearn-color-text);\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n","  /* Expand drop-down */\n","  max-height: 200px;\n","  max-width: 100%;\n","  overflow: auto;\n","}\n","\n","#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n","  content: \"▾\";\n","}\n","\n","/* Pipeline/ColumnTransformer-specific style */\n","\n","#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator-specific style */\n","\n","/* Colorize estimator box */\n","#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n","#sk-container-id-1 div.sk-label label {\n","  /* The background is the default theme color */\n","  color: var(--sklearn-color-text-on-default-background);\n","}\n","\n","/* On hover, darken the color of the background */\n","#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","/* Label box, darken color on hover, fitted */\n","#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n","  color: var(--sklearn-color-text);\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Estimator label */\n","\n","#sk-container-id-1 div.sk-label label {\n","  font-family: monospace;\n","  font-weight: bold;\n","  display: inline-block;\n","  line-height: 1.2em;\n","}\n","\n","#sk-container-id-1 div.sk-label-container {\n","  text-align: center;\n","}\n","\n","/* Estimator-specific */\n","#sk-container-id-1 div.sk-estimator {\n","  font-family: monospace;\n","  border: 1px dotted var(--sklearn-color-border-box);\n","  border-radius: 0.25em;\n","  box-sizing: border-box;\n","  margin-bottom: 0.5em;\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-0);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-0);\n","}\n","\n","/* on hover */\n","#sk-container-id-1 div.sk-estimator:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-2);\n","}\n","\n","#sk-container-id-1 div.sk-estimator.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-2);\n","}\n","\n","/* Specification for estimator info (e.g. \"i\" and \"?\") */\n","\n","/* Common style for \"i\" and \"?\" */\n","\n",".sk-estimator-doc-link,\n","a:link.sk-estimator-doc-link,\n","a:visited.sk-estimator-doc-link {\n","  float: right;\n","  font-size: smaller;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1em;\n","  height: 1em;\n","  width: 1em;\n","  text-decoration: none !important;\n","  margin-left: 1ex;\n","  /* unfitted */\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-unfitted-level-1);\n","}\n","\n",".sk-estimator-doc-link.fitted,\n","a:link.sk-estimator-doc-link.fitted,\n","a:visited.sk-estimator-doc-link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",".sk-estimator-doc-link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover,\n","div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",".sk-estimator-doc-link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","/* Span, style for the box shown on hovering the info icon */\n",".sk-estimator-doc-link span {\n","  display: none;\n","  z-index: 9999;\n","  position: relative;\n","  font-weight: normal;\n","  right: .2ex;\n","  padding: .5ex;\n","  margin: .5ex;\n","  width: min-content;\n","  min-width: 20ex;\n","  max-width: 50ex;\n","  color: var(--sklearn-color-text);\n","  box-shadow: 2pt 2pt 4pt #999;\n","  /* unfitted */\n","  background: var(--sklearn-color-unfitted-level-0);\n","  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n","}\n","\n",".sk-estimator-doc-link.fitted span {\n","  /* fitted */\n","  background: var(--sklearn-color-fitted-level-0);\n","  border: var(--sklearn-color-fitted-level-3);\n","}\n","\n",".sk-estimator-doc-link:hover span {\n","  display: block;\n","}\n","\n","/* \"?\"-specific style due to the `<a>` HTML tag */\n","\n","#sk-container-id-1 a.estimator_doc_link {\n","  float: right;\n","  font-size: 1rem;\n","  line-height: 1em;\n","  font-family: monospace;\n","  background-color: var(--sklearn-color-background);\n","  border-radius: 1rem;\n","  height: 1rem;\n","  width: 1rem;\n","  text-decoration: none;\n","  /* unfitted */\n","  color: var(--sklearn-color-unfitted-level-1);\n","  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted {\n","  /* fitted */\n","  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n","  color: var(--sklearn-color-fitted-level-1);\n","}\n","\n","/* On hover */\n","#sk-container-id-1 a.estimator_doc_link:hover {\n","  /* unfitted */\n","  background-color: var(--sklearn-color-unfitted-level-3);\n","  color: var(--sklearn-color-background);\n","  text-decoration: none;\n","}\n","\n","#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n","  /* fitted */\n","  background-color: var(--sklearn-color-fitted-level-3);\n","}\n","</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</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\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"]},"metadata":{},"execution_count":14}]},{"cell_type":"markdown","metadata":{"id":"xNkXL1YQBiBT"},"source":["## Predicting the Test set results"]},{"cell_type":"code","source":["y_pred = regressor.predict(x_test)\n","np.set_printoptions(precision=2)\n","print(np.concatenate((y_pred.reshape(len(y_pred),1),y_test.reshape(len(y_test),1)),1))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TpA9xFP0IKlQ","executionInfo":{"status":"ok","timestamp":1730421656394,"user_tz":-420,"elapsed":8,"user":{"displayName":"hưng hoàng","userId":"09018587544555875926"}},"outputId":"c63ce805-0415-4269-d833-87c017af434a"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["[[126187.39 134307.35]\n"," [ 85788.82  81005.76]\n"," [ 99777.03  99937.59]\n"," [ 45706.12  64926.08]\n"," [127062.21 125370.37]\n"," [ 51891.84  35673.41]\n"," [109114.63 105733.54]\n"," [100600.61 107404.34]\n"," [ 97954.    97427.84]\n"," [111730.58 122776.86]\n"," [128818.49 141585.52]\n"," [174195.36 166187.94]\n"," [ 93736.29  89949.14]\n"," [148381.04 152211.77]\n"," [172313.87 182901.99]]\n"]}]}]}