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في الكود الثاني، قمت بتطبيق التحجيم القياسي (StandardScaler)فقط على بيانات التدريب (x_traing) ولكن لم تقم بتطبيق نفس التحجيم على بيانات الاختبار (x_test). هذا يؤدي إلى عدم توافق في توزيع البيانات بين التدريب والاختبار، مما يسبب زيادة كبيرة في الخطأ. يجب عليك أيضًا تطبيق التحجيم على بيانات الاختبار باستخدام نفس التحجيم الذي استخدمته لبيانات التدريب: import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error data = pd.read_csv("heart_disease.csv") feutures = data.drop(['target'], axis=1, inplace=False) outpnt = data['target'] x_traing, x_test, y_traing, y_test = train_test_split(feutures, outpnt, test_size=0.25, random_state=44, shuffle=True) scaler = StandardScaler() x_scaler_traing = scaler.fit_transform(x_traing) x_scaler_test = scaler.transform(x_test) # تطبيق نفس التحجيم على بيانات الاختبار linearregression = LinearRegression(fit_intercept=True, copy_X=True, n_jobs=-1) fit = linearregression.fit(x_scaler_traing, y_traing) y_prodict = fit.predict(x_scaler_test) msevalue = mean_squared_error(y_test, y_prodict, multioutput="uniform_average") print(f"MSEvalue: {msevalue}") وتأكد من أن البيانات في ملف heart_disease.csv لا تحتوي على قيم شاذة أو غير منطقية يمكن أن تؤثر على النتائج بعد التحجيم. بتطبيق التعديلات السابقة، يجب أن تحصل على نتائج أكثر منطقية لقيمة MSE.

انا اول ما عملت كده يا أ.مصطفي فا حسابات الMSE فا كان ده النتجيه 87.53644204505163 مع العلم قبل ما اعمل كده فا كانت النتجيه 0.12410403813221675 فا اي السبيب ؟ وده الكود قبل import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error data = pd.read_csv("heart_disease.csv") feutures = data.drop(['target'] , axis=1 , inplace=False) outpnt = data['target'] x_traing , x_test , y_traing , y_test = train_test_split(feutures , outpnt , test_size=0.25, random_state=44 , shuffle=True) linearregression = LinearRegression(fit_intercept=True , copy_X=True , n_jobs=-1) fit = linearregression.fit(x_traing , y_traing) y_prodict = fit.predict(x_test) msevalue = mean_squared_error(y_test , y_prodict , multioutput="uniform_average") print(f"MSEvalue: {msevalue}") وده الكود بعد import pandas as pd from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_error data = pd.read_csv("heart_disease.csv") feutures = data.drop(['target'] , axis=1 , inplace=False) outpnt = data['target'] x_traing , x_test , y_traing , y_test = train_test_split(feutures , outpnt , test_size=0.25, random_state=44 , shuffle=True) scaler = StandardScaler() x_scaler_traing = scaler.fit_transform(x_traing) linearregression = LinearRegression(fit_intercept=True , copy_X=True , n_jobs=-1) fit = linearregression.fit(x_scaler_traing , y_traing) y_prodict = fit.predict(x_test) msevalue = mean_squared_error(y_test , y_prodict , multioutput="uniform_average") print(f"MSEvalue: {msevalue}")

اطفه المستودع القديم والجديد في لينكس منت عندي اخطاى في النطم في لبترمنل هذي التقريارير النطم

مرحباً محمد , يمكنك نتفيذ هذا الكود سيعمل كما في الصورة المرفقة , أيضاً قمت بتوضيح خطوة خطوة من خلال التعليقات : const url = 'https://alsouq.anevex.com/app/shop-api/advertisements'; const params = { home_page_position: 'Header', show_in: 'home' }; // Send the GET request axios.get(url, { params }) .then(response => { // Handle the response console.log('Data:', response.data); }) .catch(error => { // Handle any errors console.error('Error fetching data:', error); }); ايضاً لاحظ عند ارسال سيكون عنوان url بالشكل التالي لان نوع method الخاصة ب api هي GET : https://alsouq.anevex.com/app/shop-api/advertisements?home_page_position=Header&show_in=home

My pleasure, You’re welcome! 😊

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طيب اي الاحسن MSE ام RMSE ؟

وعليكم السلام Understanding the mathematical equations associated with models can be helpful for comprehending how each model works and customizing its usage effectively. However, it’s not necessary to memorize all the equations in detail. In fact, you can rely on programming libraries and available tools to execute these equations instead of memorizing them manually. For example, in the case of Linear Regression, the main equation is: [ y = \beta_0 + \beta_1 x ] Where: (y) represents the target value (dependent variable). (x) represents the independent variable. (\beta_0) and (\beta_1) are the regression coefficients. As for the RandomForestClassifier model, it relies on an ensemble of decision trees and doesn’t have a specific mathematical equation in the same way.

تمام بس سوال كمان كل نموذج ليا معادلات رياضيه فا هل موطلب مني معرفت المعادالات لكل نموذج استخدمو ؟ ان عرف LinearReagression

اختيار النموذج المناسب يعتمد على العديد من العوامل بما في ذلك طبيعة البيانات، الهدف من النموذج، والأداء المطلوب. بالتالي القول بأن نموذجًا معينًا مثل RandomForestClassifier هو دائمًا أفضل من LinearRegression غير دقيق، لأن كل منهما يخدم أغراضًا مختلفة ويعمل بشكل أفضل في ظروف معينة. لديك LinearRegression نموذج بسيط وسهل الفهم يستخدم للعلاقات الخطية بين المتغيرات المستقلة والمتغير التابع، ومناسب في حال العلاقة بين متغيراتك خطية وتحتاج إلى تفسير بسيط للنموذج. بينما RandomForestClassifier نموذج أكثر تعقيدًا يستخدم للأغراض التصنيفية، ويعمل بشكل جيد عندما تكون البيانات معقدة وتحتوي على العديد من الميزات التي قد تتفاعل مع بعضها بطرق غير خطية، ويتميز بأنه يستخدم مجموعة من الأشجار decision trees ويجمع نتائجها للحصول على تصنيف أكثر دقة. واستخدم التحقق المتبادل لتقييم أداء النموذج على مجموعة من البيانات غير المرئية للنموذج، وقد تحتاج إلى ضبط الباراميترات للنماذج المعقدة لتحسين أدائها.

وعليكم السلام The Linear Regression model and the RandomForestClassifier model differ in their use cases and the contexts where they are most beneficial.: 1Linear Regression: The Linear Regression model is used when we assume a linear relationship between the dependent variable (target) and the independent variables (predictors). It is suitable for continuous numerical data and can be used to predict target values using a straight line. Example: It can be used to predict house prices based on house size. 2Random Forest Classifier: The RandomForestClassifier model is used for classification, not continuous value prediction. It relies on an ensemble of decision trees and combines their predictions for classification. It is suitable for categorical data (such as classifying into specific categories). 3General Considerations: The choice of model depends on the nature of the data and the goal of the analysis. If you want to predict continuous values, Linear Regression is the appropriate choice. If you want to perform classification, RandomForestClassifier is the suitable choice.

السلام عليكم هل الLinearRegression مش افضل حاجه دلوقتي وهل فيه نموذك افضل من نموذج ؟ يعني مثل الRandomForestClassifier افضل من الLinearRegression والا الا علي حساب البيانات اي

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بالضبط، ففي مكتبة scikit-learn، باراميتر normalize لم يعد موجودًا في الإصدار 0.24.0 وما بعده من النموذج LinearRegression. في الإصدارات الأحدث من المكتبة، عليك استخدام StandardScaler أو Normalizer من مكتبة sklearn.preprocessing لتطبيع البيانات قبل تمريرها إلى النموذج. للتوضيح: from sklearn.linear_model import LinearRegression from sklearn.preprocessing import StandardScaler import numpy as np X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) y = np.array([1, 2, 3, 4]) scaler = StandardScaler() X_scaled = scaler.fit_transform(X) model = LinearRegression() model.fit(X_scaled, y) predictions = model.predict(X_scaled) print(predictions) لاحظ تطبيع البيانات باستخدام StandardScaler قبل استخدامها في تدريب النموذج LinearRegression، وذلك يحقق نفس النتيجة التي كان يحققها استخدام الباراميتر normalize=True في الإصدارات الأقدم.

وعليكم السلام! In the scikit-learn library, the normalize parameter is not available in the LinearRegression model. The LinearRegression model is used to fit a linear model with coefficients w = (w1, ..., wp) to minimize the sum of squared residuals between observed targets in the dataset and the predicted targets from the linear approximation If you need to apply normalization to your data before using the LinearRegression model, you can use the normalize function from scikit-learn to normalize the data. This function helps standardize the data and avoids issues related to varying scales.

مازالت الداتا ترجعلى فاضية حتى بعد تجربة الكود المتوفر من البرنامج على الرغم من ان ال postman بيرجع داتا موجودة فعلا بعد التجربة مازالت ترجع الداتا فاضية على الرغم من ان ال postman يرجع داتا حقيقية جربت الطريقة لكن ما زالت الداتا ترجع فاضية

السلام عليكم في مكتبه sklearn هو الparameters (normalize) مش موجود في الmodel LinearRegression ؟

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هذا هو الكود الذي تبحث عنه : axios.get("https://alsouq.anevex.com/app/shop-api/advertisements" , { data:{ home_page_position:"header", show_in:"home" } }) والنتيجة

توجد العديد من الطرق لكن بصفة عامة، يمكنك استخدام هذه الشيفرة ولاحظ التعليقات التي وضعتها وهي تشرح بشكل جيد دور كل سطر من البرنامج: // 1. استيراد مكتبة Axios import axios from 'axios'; // 2. تعريف الكائن JavaScript مع نفس بنية الكائن JSON في "Body => Raw" const jsonObject = { "home_page_position": "Headers", "show_in": "home" }; // 3. استخدام طريقة axios.post() لإرسال طلب POST axios.post('https://alsouq.anevex.com/app/shop-api/advertisements', jsonObject) // 4. في حالة نجاح الطلب .then(response => { // طباعة البيانات الواردة في الاستجابة على الكونسول console.log(response.data); }) // 5. في حالة وجود خطأ .catch(error => { // طباعة الخطأ على الكونسول console.error(error); }); ففي البداية نقوم باستيراد مكتبة Axios باستخدام: import axios from 'axios';. بعد ذلك، نعرّف كائنا JavaScript jsonObject بنفس بنية الكائن JSON الموجود في جزء "Body => Raw" من الصورة. ثم نستخدم طريقة axios.post() لإرسال طلب POST إلى الرابط الذي تريد وفي حالتنا https://alsouq.anevex.com/app/shop-api/advertisements. أول Argument للطريقة post() هو الرابط، وأما الثاني هو البيانات التي نريد إرسالها في جسم الطلب، وفي هذه الحالة هو الكائن jsonObject. إذا نجح الطلب، سيتم استدعاء دالة then مع استجابة الخادم ك Argument. داخل هذه الدالة، نقوم بطباعة البيانات الواردة في الاستجابة على الكونسول باستخدام console.log(response.data). إذا حدث خطأ أثناء الطلب، سيتم استدعاء دالة catch مع الخطأ ك Argument. داخل هذه الدالة، نقوم بطباعة الخطأ على الكونسول باستخدام console.error(error). عند استخدام طريقة axios.post() وتمرير الكائن jsonObject ك Argument ثانية، ستقوم Axios تلقائيا بتعيين رؤوس الطلب المناسبة (Content-Type: application/json) وترميز الكائن jsonObject كجسم الطلب.

وعليكم السلام ورحمة الله وبركاته . إرسال الطلبات requests بواسطة axios بسيط ولكنه يعتمد على نوع البيانات التي تريد إرساله . فكما في المثال السابق ستقوم بإرسالها بصيغة json والطريقة كالتالي : const json = JSON.stringify({ home_page_position: "Header",show_in:"home" }); const res = await axios.post('https://alsouq.anevex.com/app/shop-api/advertisements', json); وهكذا سيتم إرسال الطلب . أما إذا أردت إرسالها ك form سنقوم بالتالي : let data = new FormData(); data.append("home_page_position", "Header"); data.append("show_in", "home"); const res = await axios.post('https://alsouq.anevex.com/app/shop-api/advertisements', data); وهكذا سيتم إرسالها كأنه تم الإرسال من form