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+https://www.kaggle.com/datasets/elikplim/concrete-compressive-strength-data-set/data
+Dataset from Kaggle
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+Visualizations are attached here.
+Provided is a correlation matrix of utmost importance
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diff --git a/Concrete Compressive Strength/Models/Readme.Md b/Concrete Compressive Strength/Models/Readme.Md
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+**PROJECT TITLE**
+Concrete Compressive Strength Prediction Using Deep Learning
+**GOAL**
+To find best option for Concrete Compressive Strength Prediction by comparing across various models
+
+**DATASET**
+
+https://www.kaggle.com/datasets/elikplim/concrete-compressive-strength-data-set/data
+
+**DESCRIPTION**
+The project compares maximum absolute error as parameter to find the best option for Concrete Compressive Strength Prediction . Models used are XGBoostRegressor, RandomForest Regressor, LGBRegressor and Sequential Keras Model amongst others.Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and
+ingredients. These ingredients include cement, blast furnace slag, fly ash,water, superplasticizer, coarse aggregate, and fine aggregate.
+
+**WHAT I HAD DONE**
+- Used correlation matrix as EDA tool for visualizing correlation between columns
+- Used Normalizer on the columns and then performance of  various regression models like LGBMRegressor,DecisionTreeRegressor,KNeighborsRegressor and so on were compared in a table, based on RMSE, MSE, MAE,R2 SCORE
+- The hyperparameters were tuned for the most effective model to get 91.39% accuracy and 3.1 MAE
+- Now, we used Normalizer and Keras Sequential model with Dense Layers and RelU to get 3.9 MAE. Thus it's performance is comparable to LGBRegressor
+
+**MODELS USED**
+
+List out all the algorithms or models used in this project
+algo=[GradientBoostingRegressor(), lgbm.LGBMRegressor(), xg.XGBRFRegressor(),DecisionTreeRegressor(),LinearRegression(),
+     KNeighborsRegressor(),RandomForestRegressor(),BaggingRegressor(ExtraTreeRegressor(), random_state=42),
+     GaussianProcessRegressor()]
+
+The above algorithms were used and a function was written to compare the MAE, and accuracy
+
+Then a Keras Sequential model with  Dense Layers was used to compare performance of MAE, the most accurate error metric in such tasks.
+
+
+**LIBRARIES NEEDED**
+Pandas, Numpy, Keras,TensorFlow, ScikitLearn, Seaborn, Matplotlib
+
+**VISUALIZATION**
+EDA Results in Images folder
+
+**ACCURACIES**
+
+Add all the algorithms used with their accuracies and results
+
+
+**CONCLUSION**
+
+**Gradient Boosting Algorithm:**
+LightGBM is a gradient boosting framework, which is an ensemble learning technique. It builds a predictive model in the form of an ensemble of weak learners (usually decision trees), and it combines their predictions to create a stronger overall model.
+Gradient boosting algorithms, in general, are powerful for regression tasks, as they iteratively improve the model by focusing on the errors made in the previous iterations.
+
+**LightGBM's Advantages:**
+-Lightweight and Fast: LightGBM is designed to be efficient and fast. It is especially useful when dealing with large datasets or datasets with a large number of features.
+
+-Leaf-Wise Growth: LightGBM grows trees leaf-wise rather than level-wise, which can lead to a more efficient and accurate model.
+
+-Handling Non-Linear Relationships:
+Concrete compressive strength prediction has non-linear relationships between the input features and the target variable. Gradient boosting algorithms, including LightGBM, are well-suited for capturing complex, non-linear patterns in the data.
+
+Neural Networks also similar level of accuracy to LGBMRegressor.RandomForest Regressor had 2nd highest accuracy Level.The Keras Sequential model is designed for building simple feedforward neural networks. Since the data can be effectively modeled as a series of layers where information flows in one direction (from input to output), a Sequential model is a natural and straightforward choice. The level of accuracy is at part with LightGBM
+
+Lowest Absolute Error is shown for LGBRegressor and Keras Sequential Models at 3.1 and 3.9 respectively, making them the most suitable
+
+**YOUR NAME**
+Aindree Chatterjee
+
diff --git a/Concrete Compressive Strength/Models/concrete-strength-dl (1).ipynb b/Concrete Compressive Strength/Models/concrete-strength-dl (1).ipynb
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+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":3928,"sourceType":"datasetVersion","datasetId":2330}],"dockerImageVersionId":30615,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-12-10T19:36:59.371343Z","iopub.execute_input":"2023-12-10T19:36:59.371748Z","iopub.status.idle":"2023-12-10T19:36:59.746991Z","shell.execute_reply.started":"2023-12-10T19:36:59.371713Z","shell.execute_reply":"2023-12-10T19:36:59.746027Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/concrete-compressive-strength-data-set/concrete_data.csv\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport seaborn as sns\n#importing ploting libraries\nimport matplotlib.pyplot as plt\n#importing seaborn for statistical plots\nimport seaborn as sns\n#importing ploting libraries\nimport matplotlib.pyplot as plt\n#styling figures\nplt.rc('font',size=14)\nsns.set(style='white')\nsns.set(style='whitegrid',color_codes=True)\n%matplotlib inline\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.ensemble import (RandomForestRegressor, GradientBoostingRegressor,AdaBoostRegressor,BaggingRegressor)\nfrom sklearn.svm import SVR\nfrom sklearn import metrics\nfrom sklearn.ensemble import VotingRegressor\nfrom scipy import stats\nfrom sklearn.metrics import mean_squared_error,mean_absolute_error,r2_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn import preprocessing\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.utils import resample\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:36:59.748864Z","iopub.execute_input":"2023-12-10T19:36:59.749349Z","iopub.status.idle":"2023-12-10T19:37:02.059773Z","shell.execute_reply.started":"2023-12-10T19:36:59.749316Z","shell.execute_reply":"2023-12-10T19:37:02.058664Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df= pd.read_csv(\"../input/concrete-compressive-strength-data-set/concrete_data.csv\")\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:02.062088Z","iopub.execute_input":"2023-12-10T19:37:02.062511Z","iopub.status.idle":"2023-12-10T19:37:02.115692Z","shell.execute_reply.started":"2023-12-10T19:37:02.062475Z","shell.execute_reply":"2023-12-10T19:37:02.114433Z"},"trusted":true},"execution_count":3,"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   cement  blast_furnace_slag  fly_ash  water  superplasticizer  \\\n0   540.0                 0.0      0.0  162.0               2.5   \n1   540.0                 0.0      0.0  162.0               2.5   \n2   332.5               142.5      0.0  228.0               0.0   \n3   332.5               142.5      0.0  228.0               0.0   \n4   198.6               132.4      0.0  192.0               0.0   \n\n   coarse_aggregate  fine_aggregate   age  concrete_compressive_strength  \n0            1040.0            676.0   28                          79.99  \n1            1055.0            676.0   28                          61.89  \n2             932.0            594.0  270                          40.27  \n3             932.0            594.0  365                          41.05  \n4             978.4            825.5  360                          44.30  ","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>cement</th>\n      <th>blast_furnace_slag</th>\n      <th>fly_ash</th>\n      <th>water</th>\n      <th>superplasticizer</th>\n      <th>coarse_aggregate</th>\n      <th>fine_aggregate</th>\n      <th>age</th>\n      <th>concrete_compressive_strength</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>540.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>162.0</td>\n      <td>2.5</td>\n      <td>1040.0</td>\n      <td>676.0</td>\n      <td>28</td>\n      <td>79.99</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>540.0</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>162.0</td>\n      <td>2.5</td>\n      <td>1055.0</td>\n      <td>676.0</td>\n      <td>28</td>\n      <td>61.89</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>332.5</td>\n      <td>142.5</td>\n      <td>0.0</td>\n      <td>228.0</td>\n      <td>0.0</td>\n      <td>932.0</td>\n      <td>594.0</td>\n      <td>270</td>\n      <td>40.27</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>332.5</td>\n      <td>142.5</td>\n      <td>0.0</td>\n      <td>228.0</td>\n      <td>0.0</td>\n      <td>932.0</td>\n      <td>594.0</td>\n      <td>365</td>\n      <td>41.05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>198.6</td>\n      <td>132.4</td>\n      <td>0.0</td>\n      <td>192.0</td>\n      <td>0.0</td>\n      <td>978.4</td>\n      <td>825.5</td>\n      <td>360</td>\n      <td>44.30</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.columns","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:02.117699Z","iopub.execute_input":"2023-12-10T19:37:02.118137Z","iopub.status.idle":"2023-12-10T19:37:02.125217Z","shell.execute_reply.started":"2023-12-10T19:37:02.118098Z","shell.execute_reply":"2023-12-10T19:37:02.124265Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"Index(['cement', 'blast_furnace_slag', 'fly_ash', 'water', 'superplasticizer',\n       'coarse_aggregate', 'fine_aggregate ', 'age',\n       'concrete_compressive_strength'],\n      dtype='object')"},"metadata":{}}]},{"cell_type":"code","source":"fig, (ax1,ax2,ax3)=plt.subplots(1,3,figsize=(13,5))\n\n#boxplot\nsns.boxplot(x='cement',data=df,orient='v',ax=ax1)\nax1.set_ylabel('Cement', fontsize=15)\nax1.set_title('Distribution of cement', fontsize=15)\nax1.tick_params(labelsize=15)\n\n#distplot\nsns.distplot(df['cement'],ax=ax2)\nax2.set_xlabel('Cement', fontsize=15)\nax2.set_ylabel('concrete_compressive_strength', fontsize=15)\nax2.set_title('Cement vs Strength', fontsize=15)\nax2.tick_params(labelsize=15)\n\n#histogram\nax3.hist(df['cement'])\nax3.set_xlabel('Cement', fontsize=15)\nax3.set_ylabel('concrete_compressive_strength', fontsize=15)\nax3.set_title('Cement vs Strength', fontsize=15)\nax3.tick_params(labelsize=15)\n\nplt.subplots_adjust(wspace=0.5)\nplt.tight_layout()","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:02.126845Z","iopub.execute_input":"2023-12-10T19:37:02.127490Z","iopub.status.idle":"2023-12-10T19:37:03.372874Z","shell.execute_reply.started":"2023-12-10T19:37:02.127453Z","shell.execute_reply":"2023-12-10T19:37:03.371252Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1300x500 with 3 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"df2=df.drop(columns=[\"concrete_compressive_strength\"])","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:03.373873Z","iopub.execute_input":"2023-12-10T19:37:03.374194Z","iopub.status.idle":"2023-12-10T19:37:03.382267Z","shell.execute_reply.started":"2023-12-10T19:37:03.374166Z","shell.execute_reply":"2023-12-10T19:37:03.379919Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"df_columns=df2.keys()\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.preprocessing import StandardScaler,Normalizer,MinMaxScaler  #standarize the dataset to make it more useable.\nscaler =  Pipeline([('Normalizer', Normalizer()), \n#                    ('log-transformation', math.logp())\n                   ])\ndf2=scaler.fit_transform(df2);\ndf2=np.array(df2);df2","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:03.384321Z","iopub.execute_input":"2023-12-10T19:37:03.384714Z","iopub.status.idle":"2023-12-10T19:37:03.405745Z","shell.execute_reply.started":"2023-12-10T19:37:03.384689Z","shell.execute_reply":"2023-12-10T19:37:03.404373Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"array([[0.39624448, 0.        , 0.        , ..., 0.76313751, 0.49603938,\n        0.02054601],\n       [0.39293409, 0.        , 0.        , ..., 0.7676768 , 0.49189527,\n        0.02037436],\n       [0.27357162, 0.11724498, 0.        , ..., 0.76682331, 0.48872645,\n        0.22214838],\n       ...,\n       [0.12141129, 0.11397127, 0.08878967, ..., 0.72961238, 0.63771588,\n        0.02289237],\n       [0.1222549 , 0.14346317, 0.        , ..., 0.76042395, 0.60620297,\n        0.02151563],\n       [0.21646863, 0.08338481, 0.06496548, ..., 0.71727531, 0.63181625,\n        0.02323159]])"},"metadata":{}}]},{"cell_type":"code","source":"df1=pd.DataFrame(df2,columns=df_columns)\ndf1[\"concrete_compressive_strength\"]=df[\"concrete_compressive_strength\"]\ndf1","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:03.407027Z","iopub.execute_input":"2023-12-10T19:37:03.408526Z","iopub.status.idle":"2023-12-10T19:37:03.429215Z","shell.execute_reply.started":"2023-12-10T19:37:03.408479Z","shell.execute_reply":"2023-12-10T19:37:03.428302Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"        cement  blast_furnace_slag   fly_ash     water  superplasticizer  \\\n0     0.396244            0.000000  0.000000  0.118873          0.001834   \n1     0.392934            0.000000  0.000000  0.117880          0.001819   \n2     0.273572            0.117245  0.000000  0.187592          0.000000   \n3     0.268151            0.114922  0.000000  0.183875          0.000000   \n4     0.145536            0.097024  0.000000  0.140700          0.000000   \n...        ...                 ...       ...       ...               ...   \n1025  0.227316            0.095400  0.074264  0.147706          0.007320   \n1026  0.264217            0.000000  0.094797  0.160728          0.008528   \n1027  0.121411            0.113971  0.088790  0.157549          0.004987   \n1028  0.122255            0.143463  0.000000  0.134934          0.008683   \n1029  0.216469            0.083385  0.064965  0.166438          0.007135   \n\n      coarse_aggregate  fine_aggregate        age  \\\n0             0.763138         0.496039  0.020546   \n1             0.767677         0.491895  0.020374   \n2             0.766823         0.488726  0.222148   \n3             0.751630         0.479043  0.294361   \n4             0.716983         0.604936  0.263812   \n...                ...              ...       ...   \n1025          0.715584         0.631862  0.023028   \n1026          0.670711         0.667021  0.022961   \n1027          0.729612         0.637716  0.022892   \n1028          0.760424         0.606203  0.021516   \n1029          0.717275         0.631816  0.023232   \n\n      concrete_compressive_strength  \n0                             79.99  \n1                             61.89  \n2                             40.27  \n3                             41.05  \n4                             44.30  \n...                             ...  \n1025                          44.28  \n1026                          31.18  \n1027                          23.70  \n1028                          32.77  \n1029                          32.40  \n\n[1030 rows x 9 columns]","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>cement</th>\n      <th>blast_furnace_slag</th>\n      <th>fly_ash</th>\n      <th>water</th>\n      <th>superplasticizer</th>\n      <th>coarse_aggregate</th>\n      <th>fine_aggregate</th>\n      <th>age</th>\n      <th>concrete_compressive_strength</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.396244</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.118873</td>\n      <td>0.001834</td>\n      <td>0.763138</td>\n      <td>0.496039</td>\n      <td>0.020546</td>\n      <td>79.99</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.392934</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.117880</td>\n      <td>0.001819</td>\n      <td>0.767677</td>\n      <td>0.491895</td>\n      <td>0.020374</td>\n      <td>61.89</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.273572</td>\n      <td>0.117245</td>\n      <td>0.000000</td>\n      <td>0.187592</td>\n      <td>0.000000</td>\n      <td>0.766823</td>\n      <td>0.488726</td>\n      <td>0.222148</td>\n      <td>40.27</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.268151</td>\n      <td>0.114922</td>\n      <td>0.000000</td>\n      <td>0.183875</td>\n      <td>0.000000</td>\n      <td>0.751630</td>\n      <td>0.479043</td>\n      <td>0.294361</td>\n      <td>41.05</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.145536</td>\n      <td>0.097024</td>\n      <td>0.000000</td>\n      <td>0.140700</td>\n      <td>0.000000</td>\n      <td>0.716983</td>\n      <td>0.604936</td>\n      <td>0.263812</td>\n      <td>44.30</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1025</th>\n      <td>0.227316</td>\n      <td>0.095400</td>\n      <td>0.074264</td>\n      <td>0.147706</td>\n      <td>0.007320</td>\n      <td>0.715584</td>\n      <td>0.631862</td>\n      <td>0.023028</td>\n      <td>44.28</td>\n    </tr>\n    <tr>\n      <th>1026</th>\n      <td>0.264217</td>\n      <td>0.000000</td>\n      <td>0.094797</td>\n      <td>0.160728</td>\n      <td>0.008528</td>\n      <td>0.670711</td>\n      <td>0.667021</td>\n      <td>0.022961</td>\n      <td>31.18</td>\n    </tr>\n    <tr>\n      <th>1027</th>\n      <td>0.121411</td>\n      <td>0.113971</td>\n      <td>0.088790</td>\n      <td>0.157549</td>\n      <td>0.004987</td>\n      <td>0.729612</td>\n      <td>0.637716</td>\n      <td>0.022892</td>\n      <td>23.70</td>\n    </tr>\n    <tr>\n      <th>1028</th>\n      <td>0.122255</td>\n      <td>0.143463</td>\n      <td>0.000000</td>\n      <td>0.134934</td>\n      <td>0.008683</td>\n      <td>0.760424</td>\n      <td>0.606203</td>\n      <td>0.021516</td>\n      <td>32.77</td>\n    </tr>\n    <tr>\n      <th>1029</th>\n      <td>0.216469</td>\n      <td>0.083385</td>\n      <td>0.064965</td>\n      <td>0.166438</td>\n      <td>0.007135</td>\n      <td>0.717275</td>\n      <td>0.631816</td>\n      <td>0.023232</td>\n      <td>32.40</td>\n    </tr>\n  </tbody>\n</table>\n<p>1030 rows × 9 columns</p>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"corr = df1.corr()\nsns.heatmap(corr,annot=True,vmax=1,vmin=-1)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:03.430916Z","iopub.execute_input":"2023-12-10T19:37:03.431462Z","iopub.status.idle":"2023-12-10T19:37:03.955331Z","shell.execute_reply.started":"2023-12-10T19:37:03.431428Z","shell.execute_reply":"2023-12-10T19:37:03.953920Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\n# from sklearn.inspection import permutation_importance\nfrom sklearn.model_selection import train_test_split\nX = df1.drop(columns=[\"concrete_compressive_strength\"])\ny = df1.concrete_compressive_strength\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42,shuffle=True)","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:03.958487Z","iopub.execute_input":"2023-12-10T19:37:03.959336Z","iopub.status.idle":"2023-12-10T19:37:03.969462Z","shell.execute_reply.started":"2023-12-10T19:37:03.959303Z","shell.execute_reply":"2023-12-10T19:37:03.968164Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"!pip install statsmodels","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:03.971404Z","iopub.execute_input":"2023-12-10T19:37:03.971792Z","iopub.status.idle":"2023-12-10T19:37:16.259880Z","shell.execute_reply.started":"2023-12-10T19:37:03.971762Z","shell.execute_reply":"2023-12-10T19:37:16.258342Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Requirement already satisfied: statsmodels in /opt/conda/lib/python3.10/site-packages (0.14.0)\nRequirement already satisfied: numpy>=1.18 in /opt/conda/lib/python3.10/site-packages (from statsmodels) (1.24.3)\nRequirement already satisfied: scipy!=1.9.2,>=1.4 in /opt/conda/lib/python3.10/site-packages (from statsmodels) (1.11.4)\nRequirement already satisfied: pandas>=1.0 in /opt/conda/lib/python3.10/site-packages (from statsmodels) (2.0.3)\nRequirement already satisfied: patsy>=0.5.2 in /opt/conda/lib/python3.10/site-packages (from statsmodels) (0.5.3)\nRequirement already satisfied: packaging>=21.3 in /opt/conda/lib/python3.10/site-packages (from statsmodels) (21.3)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=21.3->statsmodels) (3.0.9)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0->statsmodels) (2.8.2)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0->statsmodels) (2023.3)\nRequirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.0->statsmodels) (2023.3)\nRequirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from patsy>=0.5.2->statsmodels) (1.16.0)\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install lightgbm\n!pip install xgboost","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:16.261279Z","iopub.execute_input":"2023-12-10T19:37:16.261744Z","iopub.status.idle":"2023-12-10T19:37:36.893198Z","shell.execute_reply.started":"2023-12-10T19:37:16.261705Z","shell.execute_reply":"2023-12-10T19:37:36.891917Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Requirement already satisfied: lightgbm in /opt/conda/lib/python3.10/site-packages (3.3.2)\nRequirement already satisfied: wheel in /opt/conda/lib/python3.10/site-packages (from lightgbm) (0.41.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from lightgbm) (1.24.3)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from lightgbm) (1.11.4)\nRequirement already satisfied: scikit-learn!=0.22.0 in /opt/conda/lib/python3.10/site-packages (from lightgbm) (1.2.2)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn!=0.22.0->lightgbm) (1.3.2)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn!=0.22.0->lightgbm) (3.2.0)\nRequirement already satisfied: xgboost in /opt/conda/lib/python3.10/site-packages (2.0.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from xgboost) (1.24.3)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from xgboost) (1.11.4)\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error,mean_squared_error,r2_score\nfrom sklearn.ensemble import GradientBoostingRegressor\nimport lightgbm as lgbm\nimport xgboost as xg\nfrom sklearn.compose import TransformedTargetRegressor\nfrom sklearn.preprocessing import QuantileTransformer\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.ensemble import BaggingRegressor\nfrom sklearn.tree import ExtraTreeRegressor\nfrom sklearn.gaussian_process import GaussianProcessRegressor\n# from sklearn.linear_model import LogisticRegression\n\n#generic function to fit model and return metrics for every algorithm\ndef boost_models(x):\n    #transforming target variable through quantile transformer\n    regr_trans = TransformedTargetRegressor(regressor=x, transformer=QuantileTransformer(output_distribution='normal'))\n    regr_trans.fit(X_train, y_train)\n    yhat = regr_trans.predict(X_test)\n    algoname= x.__class__.__name__\n    return algoname, round(r2_score(y_test, yhat),3), round(mean_absolute_error(y_test, yhat),2), round(np.sqrt(mean_squared_error(y_test, yhat)),2),round(mean_squared_error(y_test,yhat),2)\n\nalgo=[GradientBoostingRegressor(), lgbm.LGBMRegressor(), xg.XGBRFRegressor(),DecisionTreeRegressor(),LinearRegression(),\n     KNeighborsRegressor(),RandomForestRegressor(),BaggingRegressor(ExtraTreeRegressor(), random_state=42),\n     GaussianProcessRegressor()]\nscore=[]\nfor a in algo:\n    score.append(boost_models(a))\n\n #Collate all scores in a table\npd.DataFrame(score, columns=['Model', 'Score', 'MAE', 'RMSE','MSE'])","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:36.894767Z","iopub.execute_input":"2023-12-10T19:37:36.895148Z","iopub.status.idle":"2023-12-10T19:37:40.700042Z","shell.execute_reply.started":"2023-12-10T19:37:36.895121Z","shell.execute_reply":"2023-12-10T19:37:40.696733Z"},"trusted":true},"execution_count":13,"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"                       Model  Score   MAE  RMSE    MSE\n0  GradientBoostingRegressor  0.873  4.27  5.87  34.48\n1              LGBMRegressor  0.892  3.36  5.41  29.31\n2             XGBRFRegressor  0.819  5.16  7.01  49.10\n3      DecisionTreeRegressor  0.800  4.93  7.37  54.30\n4           LinearRegression  0.671  7.33  9.43  89.00\n5        KNeighborsRegressor  0.719  6.79  8.73  76.19\n6      RandomForestRegressor  0.881  3.96  5.67  32.15\n7           BaggingRegressor  0.873  4.05  5.88  34.53\n8   GaussianProcessRegressor  0.834  4.60  6.71  44.99","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>Model</th>\n      <th>Score</th>\n      <th>MAE</th>\n      <th>RMSE</th>\n      <th>MSE</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GradientBoostingRegressor</td>\n      <td>0.873</td>\n      <td>4.27</td>\n      <td>5.87</td>\n      <td>34.48</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>LGBMRegressor</td>\n      <td>0.892</td>\n      <td>3.36</td>\n      <td>5.41</td>\n      <td>29.31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>XGBRFRegressor</td>\n      <td>0.819</td>\n      <td>5.16</td>\n      <td>7.01</td>\n      <td>49.10</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>DecisionTreeRegressor</td>\n      <td>0.800</td>\n      <td>4.93</td>\n      <td>7.37</td>\n      <td>54.30</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>LinearRegression</td>\n      <td>0.671</td>\n      <td>7.33</td>\n      <td>9.43</td>\n      <td>89.00</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>KNeighborsRegressor</td>\n      <td>0.719</td>\n      <td>6.79</td>\n      <td>8.73</td>\n      <td>76.19</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>RandomForestRegressor</td>\n      <td>0.881</td>\n      <td>3.96</td>\n      <td>5.67</td>\n      <td>32.15</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>BaggingRegressor</td>\n      <td>0.873</td>\n      <td>4.05</td>\n      <td>5.88</td>\n      <td>34.53</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>GaussianProcessRegressor</td>\n      <td>0.834</td>\n      <td>4.60</td>\n      <td>6.71</td>\n      <td>44.99</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"from lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\n# from lightgbm import LGBMRegressor\n# regr = RandomForestRegressor(max_depth=2, random_state=42)\nregr = LGBMRegressor(n_estimators=1000, max_depth=20, eta=0.1, subsample=0.7, colsample_bytree=0.8,learning_rate=.1,reg_lambda=0.2)\n#best_model\nregr.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:40.702294Z","iopub.execute_input":"2023-12-10T19:37:40.702731Z","iopub.status.idle":"2023-12-10T19:37:50.120293Z","shell.execute_reply.started":"2023-12-10T19:37:40.702692Z","shell.execute_reply":"2023-12-10T19:37:50.118797Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"[LightGBM] [Warning] learning_rate is set=0.1, eta=0.1 will be ignored. Current value: learning_rate=0.1\n","output_type":"stream"},{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"LGBMRegressor(colsample_bytree=0.8, eta=0.1, max_depth=20, n_estimators=1000,\n              reg_lambda=0.2, subsample=0.7)","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: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: 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>LGBMRegressor(colsample_bytree=0.8, eta=0.1, max_depth=20, n_estimators=1000,\n              reg_lambda=0.2, subsample=0.7)</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 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 sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(colsample_bytree=0.8, eta=0.1, max_depth=20, n_estimators=1000,\n              reg_lambda=0.2, subsample=0.7)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"y_pred=regr.predict(X_test);y_pred","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:50.123696Z","iopub.execute_input":"2023-12-10T19:37:50.124065Z","iopub.status.idle":"2023-12-10T19:37:50.147302Z","shell.execute_reply.started":"2023-12-10T19:37:50.124028Z","shell.execute_reply":"2023-12-10T19:37:50.146206Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"array([53.34887734, 39.43235961, 76.37623394, 35.27790472, 10.99048232,\n       45.27745286, 24.79265707, 51.81585737, 32.75211926, 44.89312136,\n       38.40100177, 11.84360681, 40.73126859, 47.96752783, 23.58800849,\n       24.14710816, 39.81008396, 19.20612159, 37.24810503, 34.37521431,\n       33.80203041, 40.33618849, 48.37968407,  8.19155006, 32.37477662,\n       38.31847098,  6.96309848, 43.21795604, 53.55513589, 14.6349889 ,\n       53.78758926, 36.83965525, 49.30032844, 58.22367907, 18.01393026,\n       35.30628124, 32.9044962 , 42.30925184, 13.06335044, 50.00087171,\n       13.3056119 ,  6.05802639, 34.89371098, 44.42642803, 13.70856279,\n       65.61508908, 46.89197321, 38.7354274 , 23.55130392,  4.56679376,\n       56.61647869, 42.57972486, 26.92650438, 15.04789703, 42.00110053,\n       31.73935071, 28.24267965, 12.32611317, 30.48152044, 20.52659355,\n       42.02821129, 13.51199799, 39.15808622, 50.63626215, 32.3606777 ,\n       16.82199326, 34.71575411, 13.20848023, 32.49251564, 24.02887794,\n       10.69589605, 25.9520917 ,  8.6990315 , 38.4227679 , 27.52025462,\n       12.74854764, 55.02172242, 53.40806208, 56.98701668,  9.87794515,\n       41.94591722, 39.30883956, 34.55622135, 36.21442821, 43.40688224,\n       35.27790472, 42.33802095, 35.11671524, 24.61617229, 22.5141105 ,\n       33.94644247, 72.43411123,  9.16192896, 56.65604137, 37.73795771,\n       49.29014614, 25.96301587, 41.67800196, 20.94709776, 36.03600821,\n       25.41033273, 43.18856004, 36.84546895, 24.59125913, 71.03795972,\n       11.28544059, 53.58232808, 32.37617073, 33.83460287, 63.12483335,\n       43.57359317, 51.82533309, 29.21421078, 41.6746045 , 35.73300107,\n       55.2608447 , 18.27794174, 33.82643606, 58.57946005, 37.73795771,\n       21.60944671, 27.62322808, 50.0073295 , 28.40326026, 25.49836227,\n       40.76049023, 49.25588175, 49.19861812, 47.43788272, 33.27963731,\n       11.29189697, 35.64135183, 20.83610887, 73.01695992,  6.85944224,\n       41.29384844, 32.78079263, 41.37383034, 21.41914619, 36.0259401 ,\n       34.32125486, 31.93499363, 13.33574383, 29.05342343, 45.43242878,\n       42.79880651, 26.60154044, 12.70524018,  7.40029603, 18.20740492,\n       43.44224738, 18.10747056, 40.67903567, 24.30317411, 43.79929011,\n       39.33914287, 24.03840897, 72.03291904, 39.43235961, 41.93406786,\n       14.93621708, 59.32801629, 35.92658529, 53.60681628, 33.43982672,\n       68.75735362, 39.55417543, 24.580653  , 38.6771568 , 18.58932707,\n       49.35609078, 16.60933078, 24.49986151, 40.17457521, 36.50809768,\n        9.26594566, 31.27466427, 48.1187975 , 42.21243236, 38.26079649,\n       35.75483018, 26.1263614 , 63.12483335, 33.80171008, 41.54441409,\n       53.67076334, 24.99748803, 36.17547794, 24.72902398, 14.57589329,\n       24.18740894, 42.70349903, 39.4909989 , 45.71569669, 27.4393186 ,\n       27.66331026, 50.52491502, 18.00987106, 20.30923474, 20.93502697,\n       41.88755035, 55.2608447 , 21.00885883, 36.12456441, 70.04174605,\n       37.99770344, 27.61644735, 61.11120857, 24.55709917, 34.65562913,\n       13.39153888, 24.70365852, 20.73284617, 44.19263592, 40.26242454,\n        3.69203451, 13.40364485, 36.67048111, 21.70818624, 71.30252883,\n       22.83223997, 62.4958899 , 59.51836464, 17.5844004 , 43.2618926 ,\n       18.73863793, 49.87009465, 18.24873707, 23.50834826, 28.68919288,\n       14.48919081, 11.03025111, 38.90811694, 47.3523403 , 40.4686985 ,\n       33.92312612, 26.49054533, 33.04792901, 52.5577981 , 16.16567394,\n       33.3472264 , 46.11343536, 53.90206703, 34.38507267, 67.26693304,\n       30.34180512, 71.68015554, 19.15693486, 54.44380206, 64.28736643,\n       33.94328411, 39.43923925, 41.90613769, 24.46415139, 39.00067453,\n       15.13114846, 36.76770908, 17.736724  ])"},"metadata":{}}]},{"cell_type":"code","source":"mae = mean_absolute_error(y_true=y_test,y_pred=y_pred)\n#squared True returns MSE value, False returns RMSE value.\nmse = mean_squared_error(y_true=y_test,y_pred=y_pred) #default=True\nrmse = mean_squared_error(y_true=y_test,y_pred=y_pred,squared=False)\n  \nprint(\"MAE:\",mae)\nprint(\"MSE:\",mse)\nprint(\"RMSE:\",rmse)\nprint(\"r2_score\",round(r2_score(y_test, y_pred), 4)*100)","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:50.148301Z","iopub.execute_input":"2023-12-10T19:37:50.149670Z","iopub.status.idle":"2023-12-10T19:37:50.158283Z","shell.execute_reply.started":"2023-12-10T19:37:50.149606Z","shell.execute_reply":"2023-12-10T19:37:50.157461Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"MAE: 3.1013174326657187\nMSE: 23.312466209060776\nRMSE: 4.8282984796987\nr2_score 91.39\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras.layers.experimental import preprocessing\n\nnormalizer = preprocessing.Normalization()\nnormalizer.adapt(X)\n\ndnn = keras.models.Sequential([\n    normalizer,\n    keras.layers.Dense(64, activation='relu'),\n    keras.layers.Dense(64, activation='relu'),\n    keras.layers.Dense(1)\n])\ndnn.compile(loss='mean_absolute_error', optimizer=keras.optimizers.Adam(0.001))\ndnn.summary()","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:37:50.159277Z","iopub.execute_input":"2023-12-10T19:37:50.160682Z","iopub.status.idle":"2023-12-10T19:38:05.846737Z","shell.execute_reply.started":"2023-12-10T19:37:50.160630Z","shell.execute_reply":"2023-12-10T19:38:05.845010Z"},"trusted":true},"execution_count":17,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n normalization (Normalizati  (None, 8)                 17        \n on)                                                             \n                                                                 \n dense (Dense)               (None, 64)                576       \n                                                                 \n dense_1 (Dense)             (None, 64)                4160      \n                                                                 \n dense_2 (Dense)             (None, 1)                 65        \n                                                                 \n=================================================================\nTotal params: 4818 (18.82 KB)\nTrainable params: 4801 (18.75 KB)\nNon-trainable params: 17 (72.00 Byte)\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"dnn.fit(X_train,y_train,epochs=100,batch_size=1,validation_data=(X_test,y_test))","metadata":{"execution":{"iopub.status.busy":"2023-12-10T19:38:05.848754Z","iopub.execute_input":"2023-12-10T19:38:05.849223Z","iopub.status.idle":"2023-12-10T19:40:28.457343Z","shell.execute_reply.started":"2023-12-10T19:38:05.849187Z","shell.execute_reply":"2023-12-10T19:40:28.456035Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Epoch 1/100\n772/772 [==============================] - 2s 2ms/step - loss: 15.7072 - val_loss: 10.4688\nEpoch 2/100\n772/772 [==============================] - 1s 1ms/step - loss: 10.2634 - val_loss: 9.3378\nEpoch 3/100\n772/772 [==============================] - 1s 2ms/step - loss: 9.2592 - val_loss: 8.8456\nEpoch 4/100\n772/772 [==============================] - 1s 1ms/step - loss: 8.4710 - val_loss: 7.5966\nEpoch 5/100\n772/772 [==============================] - 1s 2ms/step - loss: 7.5526 - val_loss: 6.7016\nEpoch 6/100\n772/772 [==============================] - 1s 2ms/step - loss: 6.4859 - val_loss: 6.3095\nEpoch 7/100\n772/772 [==============================] - 1s 2ms/step - loss: 5.8922 - val_loss: 6.5044\nEpoch 8/100\n772/772 [==============================] - 1s 2ms/step - loss: 5.2968 - val_loss: 5.6377\nEpoch 9/100\n772/772 [==============================] - 1s 2ms/step - loss: 5.1484 - val_loss: 5.1183\nEpoch 10/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.9579 - val_loss: 5.4211\nEpoch 11/100\n772/772 [==============================] - 1s 1ms/step - loss: 4.8634 - val_loss: 5.0293\nEpoch 12/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.5862 - val_loss: 5.5057\nEpoch 13/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.6163 - val_loss: 4.8473\nEpoch 14/100\n772/772 [==============================] - 1s 1ms/step - loss: 4.5044 - val_loss: 4.7972\nEpoch 15/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.4391 - val_loss: 4.6859\nEpoch 16/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.3076 - val_loss: 5.0015\nEpoch 17/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.3555 - val_loss: 4.6192\nEpoch 18/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.1481 - val_loss: 4.7812\nEpoch 19/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.3268 - val_loss: 4.9633\nEpoch 20/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.2161 - val_loss: 4.6737\nEpoch 21/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.1707 - val_loss: 4.3741\nEpoch 22/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.2201 - val_loss: 4.3917\nEpoch 23/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.1195 - val_loss: 4.5077\nEpoch 24/100\n772/772 [==============================] - 1s 1ms/step - loss: 4.1440 - val_loss: 4.6564\nEpoch 25/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.1719 - val_loss: 4.4151\nEpoch 26/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.1242 - val_loss: 4.9525\nEpoch 27/100\n772/772 [==============================] - 1s 1ms/step - loss: 4.0460 - val_loss: 4.3083\nEpoch 28/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.0307 - val_loss: 4.3286\nEpoch 29/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.9468 - val_loss: 4.9120\nEpoch 30/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.9857 - val_loss: 4.7022\nEpoch 31/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.8563 - val_loss: 4.7034\nEpoch 32/100\n772/772 [==============================] - 1s 1ms/step - loss: 3.9078 - val_loss: 4.4090\nEpoch 33/100\n772/772 [==============================] - 1s 2ms/step - loss: 4.0041 - val_loss: 5.0488\nEpoch 34/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.9320 - val_loss: 4.8800\nEpoch 35/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.9402 - val_loss: 4.5907\nEpoch 36/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.8797 - val_loss: 4.3323\nEpoch 37/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.8233 - val_loss: 4.2721\nEpoch 38/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.8198 - val_loss: 4.4677\nEpoch 39/100\n772/772 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[==============================] - 1s 2ms/step - loss: 3.4740 - val_loss: 3.9689\nEpoch 59/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.3504 - val_loss: 4.4543\nEpoch 60/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.4778 - val_loss: 4.0937\nEpoch 61/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.4962 - val_loss: 4.4143\nEpoch 62/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.4345 - val_loss: 4.1371\nEpoch 63/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.3700 - val_loss: 4.5708\nEpoch 64/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.3535 - val_loss: 4.1530\nEpoch 65/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.3690 - val_loss: 4.1730\nEpoch 66/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.3998 - val_loss: 4.1587\nEpoch 67/100\n772/772 [==============================] - 1s 1ms/step - loss: 3.3829 - 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[==============================] - 1s 2ms/step - loss: 3.2683 - val_loss: 4.1769\nEpoch 78/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.2331 - val_loss: 3.8558\nEpoch 79/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.2377 - val_loss: 4.3618\nEpoch 80/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.1511 - val_loss: 4.3604\nEpoch 81/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.1856 - val_loss: 3.8765\nEpoch 82/100\n772/772 [==============================] - 1s 1ms/step - loss: 3.1498 - val_loss: 3.9967\nEpoch 83/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.1864 - val_loss: 4.2062\nEpoch 84/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.1030 - val_loss: 3.9838\nEpoch 85/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.0900 - val_loss: 4.3963\nEpoch 86/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.1655 - 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[==============================] - 1s 2ms/step - loss: 2.9661 - val_loss: 3.8667\nEpoch 97/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.0280 - val_loss: 3.7777\nEpoch 98/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.0356 - val_loss: 3.8366\nEpoch 99/100\n772/772 [==============================] - 1s 2ms/step - loss: 2.9265 - val_loss: 3.6647\nEpoch 100/100\n772/772 [==============================] - 1s 2ms/step - loss: 3.0184 - val_loss: 3.7141\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"<keras.src.callbacks.History at 0x7fe1b3de26b0>"},"metadata":{}}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}
\ No newline at end of file
diff --git a/Concrete Compressive Strength/requirements.txt b/Concrete Compressive Strength/requirements.txt
new file mode 100644
index 000000000..e40442aa2
--- /dev/null
+++ b/Concrete Compressive Strength/requirements.txt	
@@ -0,0 +1,7 @@
+Tensorflow
+Keras
+Seaborn
+Numpy
+Pandas
+ScikitLearn
+The above libraries are needed