diff --git a/Machine Learning Final Project DTA data.ipynb b/Machine Learning Final Project DTA data.ipynb
new file mode 100644
index 0000000..47b2021
--- /dev/null
+++ b/Machine Learning Final Project DTA data.ipynb	
@@ -0,0 +1 @@
+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Machine Learning Final Project DTA data.ipynb","provenance":[{"file_id":"1k987y4Sj4mvueMqj9d-InjiUeK1611Yn","timestamp":1569961189320}]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"hsd_fYYqohhi","colab_type":"code","outputId":"3492c78a-55b9-42d4-9201-1a12f1e3c211","executionInfo":{"status":"ok","timestamp":1570032980054,"user_tz":240,"elapsed":102901,"user":{"displayName":"Sean Orzolek","photoUrl":"","userId":"13907533065611641698"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","# importing os module \n","import os \n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.datasets import make_classification\n","\n","#Load in data\n","os.chdir(\"//content//gdrive//My Drive//PhD casting project//Summary files\")\n","\n","filename='DTAdatatrimtrain.csv'\n","filename1='DTAdatatrimtest.csv'\n","\n","data = pd.read_csv(filename)\n","data.head()\n","data.info()\n","tdata = pd.read_csv(filename1)\n","#F_MC=data['Fraction MC E1']\n","\n","## Define Data of Interest\n","labels=data['Sample ID']\n","liquidus=data['Liquidus']\n","tliquidus=tdata['Liquidus']\n","comp=data.loc[:,'C':'Fe']\n","tcomp=tdata.loc[:,'C':'Fe']\n","print(comp)\n","print(liquidus)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n","<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 27 entries, 0 to 26\n","Data columns (total 16 columns):\n","Sample ID    27 non-null object\n","C            27 non-null float64\n","Cr           27 non-null float64\n","Ni           27 non-null float64\n","Mn           27 non-null float64\n","Si           27 non-null float64\n","Mo           27 non-null float64\n","Nb           27 non-null float64\n","W            27 non-null float64\n","Ti           27 non-null float64\n","Zr           27 non-null float64\n","Fe           27 non-null float64\n","Liquidus     27 non-null float64\n","Te1          27 non-null object\n","Te2          27 non-null float64\n","STR          27 non-null float64\n","dtypes: float64(14), object(2)\n","memory usage: 3.5+ KB\n","        C      Cr      Ni     Mn     Si  ...     Nb       W      Ti    Zr       Fe\n","0   0.485  23.245  33.761  0.972  0.765  ...  0.393  0.1050  0.0660  0.00  39.6340\n","1   0.440  23.632  34.191  0.913  0.773  ...  0.380  0.1380  0.0670  0.00  38.9870\n","2   0.450  26.732  34.639  0.829  1.503  ...  1.036  0.0000  0.0770  0.00  34.6500\n","3   0.410  25.940  35.640  0.960  2.000  ...  1.110  0.0900  0.0300  0.12  33.1500\n","4   0.410  25.690  36.040  0.900  1.900  ...  1.110  0.0900  0.0400  0.14  33.1100\n","5   0.420  25.390  35.710  0.950  2.000  ...  1.110  0.0800  0.0400  0.10  33.5900\n","6   0.430  25.000  35.000  0.600  1.300  ...  0.800  0.1260  0.1300  0.19  36.0475\n","7   0.430  25.000  34.000  0.600  1.300  ...  1.110  0.0910  0.1300  0.17  36.8251\n","8   0.450  26.000  35.000  0.700  1.300  ...  0.800  0.1130  0.1300  0.18  35.6654\n","9   0.440  24.930  34.000  1.260  1.380  ...  1.300  0.0000  0.0000  0.00  36.6100\n","10  0.326  24.700  34.400  0.912  1.120  ...  0.404  0.0878  0.0057  0.00  37.7624\n","11  0.523  25.200  35.000  0.899  1.140  ...  0.418  0.0879  0.0064  0.00  36.4539\n","12  0.343  24.290  34.420  0.945  1.210  ...  0.994  0.0425  0.0065  0.00  37.5269\n","13  0.514  25.800  34.700  0.932  1.160  ...  1.150  0.0387  0.0074  0.00  35.4800\n","14  0.336  25.200  34.600  0.901  1.140  ...  1.470  0.0305  0.0081  0.00  36.0724\n","15  0.554  25.500  34.500  0.942  1.150  ...  1.480  0.0336  0.0087  0.00  35.6089\n","16  0.499  25.000  35.400  1.120  0.807  ...  0.978  0.0181  0.0081  0.00  35.9586\n","17  0.425  25.500  34.900  0.848  1.310  ...  0.922  0.0379  0.0080  0.00  35.8201\n","18  0.445  25.400  34.100  0.933  2.570  ...  1.010  0.0685  0.0095  0.00  35.1628\n","19  0.407  24.800  34.100  0.923  1.120  ...  1.610  0.0317  0.1140  0.00  36.6350\n","20  0.430  25.000  34.000  0.790  1.200  ...  0.910  1.5900  0.1380  0.00  35.9922\n","21  0.448  24.800  34.700  0.853  1.050  ...  1.440  1.4500  0.0195  0.00  34.9732\n","22  0.450  25.000  34.000  0.940  1.200  ...  0.960  0.0910  0.1300  0.00  37.3938\n","23  0.430  25.000  34.000  0.990  1.210  ...  1.500  1.5400  0.1560  0.00  35.4232\n","24  0.424  25.200  34.000  0.892  1.220  ...  1.450  0.0628  0.0091  0.00  36.4819\n","25  0.452  24.900  34.400  0.900  1.120  ...  0.944  0.0342  0.0084  0.00  36.9844\n","26  0.430  25.300  34.700  0.976  1.150  ...  0.506  0.0638  0.0070  0.00  36.5904\n","\n","[27 rows x 11 columns]\n","0     1382.850000\n","1     1382.400000\n","2     1362.550000\n","3     1351.600000\n","4     1352.150000\n","5     1352.350000\n","6     1367.550000\n","7     1370.400000\n","8     1370.650000\n","9     1359.900000\n","10    1383.300000\n","11    1368.400000\n","12    1375.400000\n","13    1361.500000\n","14    1366.000000\n","15    1356.700000\n","16    1371.900000\n","17    1365.200000\n","18    1348.850000\n","19    1364.050000\n","20    1366.150000\n","21    1361.766667\n","22    1369.600000\n","23    1357.050000\n","24    1367.800000\n","25    1368.200000\n","26    1374.800000\n","Name: Liquidus, dtype: float64\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tbBL8aI11Oll","colab_type":"code","colab":{}},"source":["# %% Visualize Data\n","plt.figure(figsize=(10,6))\n","plt.scatter(data['C'],data['Liquidus'])\n","plt.xlabel('Concentration (wt.%)')\n","plt.ylabel('Temperature (C)')\n","plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7slpzDth25jB","colab_type":"code","outputId":"e2537815-fdac-4eb6-d12e-cd2a9ba41e9d","executionInfo":{"status":"ok","timestamp":1570032987276,"user_tz":240,"elapsed":584,"user":{"displayName":"Sean Orzolek","photoUrl":"","userId":"13907533065611641698"}},"colab":{"base_uri":"https://localhost:8080/","height":422}},"source":["  # Trees!\n","\n","from sklearn.ensemble import RandomForestRegressor\n","from sklearn.datasets import make_regression\n","\n","X=comp\n","y=liquidus\n","#X, y = make_regression(n_features=4, n_informative=2,\n","#                       random_state=0, shuffle=False)\n","\n","regr = RandomForestRegressor(max_depth=20, random_state=0,\n","                             n_estimators=100)\n","\n","regr.fit(X, y)  \n","\n","print(regr.feature_importances_)\n","\n","print(regr.predict(tcomp))\n","print(tliquidus)\n","\n","plt.scatter(tliquidus,regr.predict(tcomp))\n","plt.plot([1350,1400],[1350,1400])\n","plt.ylim(1350,1400)\n","plt.xlim(1350,1400)\n","plt.xlabel('Measured Temperature (C)')\n","plt.ylabel('Predicted Temperature (C)')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["[0.01670288 0.08567572 0.00881047 0.03942633 0.13336387 0.02787641\n"," 0.18501999 0.01289004 0.00702579 0.00138209 0.4818264 ]\n","[1368.957      1352.84250002 1367.56650001]\n","0    1362.80\n","1    1357.85\n","2    1363.90\n","Name: Liquidus, dtype: float64\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Predicted Temperature (C)')"]},"metadata":{"tags":[]},"execution_count":3},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZgAAAEKCAYAAAAvlUMdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nL853AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}
\ No newline at end of file