{"id":6,"date":"2023-07-08T14:00:00","date_gmt":"2023-07-08T13:00:00","guid":{"rendered":"https:\/\/machinelearning.com.ng\/?p=6"},"modified":"2023-07-08T08:04:18","modified_gmt":"2023-07-08T07:04:18","slug":"building-a-linear-regression-model-for-a-position-and-salary-relationship","status":"publish","type":"post","link":"https:\/\/machinelearning.com.ng\/?p=6","title":{"rendered":"Building a Linear Regression Model for a position and salary relationship."},"content":{"rendered":"\n<pre class=\"wp-block-code\"><code>\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n     \n\ndata = pd.read_csv('Position_Salaries.csv')\nindex = {'Business Analyst': 0, 'Junior Consultant': 1, 'Senior Consultant': 2, 'Manager': 3, 'Country Manager': 4, 'Region Manager': 5, 'Partner': 6, 'Senior Partner': 7, 'C-level': 8, 'CEO': 9}\ndata&#91;'Position'] = data&#91;'Position'].map(index)\nX = data.iloc&#91;:, 1:-1].values\ny = data.iloc&#91;:, -1].values\nprint(X)\nprint(y)\n     \n&#91;&#91; 1]\n &#91; 2]\n &#91; 3]\n &#91; 4]\n &#91; 5]\n &#91; 6]\n &#91; 7]\n &#91; 8]\n &#91; 9]\n &#91;10]]\n&#91;  45000   50000   60000   80000  110000  150000  200000  300000  500000\n 1000000]\n\ny = y.reshape(len(y), 1)\nprint(y)\n     \n&#91;&#91;  45000]\n &#91;  50000]\n &#91;  60000]\n &#91;  80000]\n &#91; 110000]\n &#91; 150000]\n &#91; 200000]\n &#91; 300000]\n &#91; 500000]\n &#91;1000000]]\n\ndata.corr()\n     \nPosition\tLevel\tSalary\nPosition\t1.000000\t1.000000\t0.817949\nLevel\t1.000000\t1.000000\t0.817949\nSalary\t0.817949\t0.817949\t1.000000\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n     \n\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)\n     \nLinearRegression()\n\ny_pred = regressor.predict(X_test)\n     \n\nplt.figure(figsize=(15, 4))\nplt.subplot(1, 2, 1)\nplt.scatter(X_train, y_train, color='red')\nplt.plot(X_train, regressor.predict(X_train), color='blue')\nplt.xlabel('Salary')\nplt.ylabel('Level')\n\nplt.subplot(1, 2, 2)\nplt.scatter(X_train, y_train, color='red')\nplt.plot(X_train, regressor.predict(X_train), color='blue')\nplt.xlabel('Salary')\nplt.ylabel('Position')\n\nplt.suptitle('Position and Salaries')\nplt.show()<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"906\" height=\"291\" src=\"https:\/\/machinelearning.com.ng\/wp-content\/uploads\/2023\/07\/Linear-Regression-Model-for-Position-and-Salaries-graph.png\" alt=\"\" class=\"wp-image-8\" srcset=\"https:\/\/machinelearning.com.ng\/wp-content\/uploads\/2023\/07\/Linear-Regression-Model-for-Position-and-Salaries-graph.png 906w, https:\/\/machinelearning.com.ng\/wp-content\/uploads\/2023\/07\/Linear-Regression-Model-for-Position-and-Salaries-graph-300x96.png 300w, https:\/\/machinelearning.com.ng\/wp-content\/uploads\/2023\/07\/Linear-Regression-Model-for-Position-and-Salaries-graph-768x247.png 768w\" sizes=\"(max-width: 906px) 100vw, 906px\" \/><\/figure>\n\n\n\n<pre class=\"wp-block-code\"><code>np.set_printoptions(precision=2)\r\nprint(np.concatenate((y_pred.reshape(len(y_pred), 1), y_test.reshape(len(y_test), 1)), 1))\r\n     \r\n&#91;&#91; 44275.93  60000.  ]\r\n &#91;543473.58 500000.  ]]\r\n\r\nfrom sklearn.metrics import r2_score\r\nr2_score(y_test, y_pred)\r\n     \r\n0.9779215014976274<\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<p>Building a machine learning model to predict the relationship between positions and salary.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_mi_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[6,5],"tags":[8,7,9],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=\/wp\/v2\/posts\/6"}],"collection":[{"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=6"}],"version-history":[{"count":3,"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=\/wp\/v2\/posts\/6\/revisions"}],"predecessor-version":[{"id":11,"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=\/wp\/v2\/posts\/6\/revisions\/11"}],"wp:attachment":[{"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/machinelearning.com.ng\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}