Before applying any machine learning make sure you preprocess the text, do standardization, convert text to numerical vectors. Below are the steps you may follow if you are just starting out(beginner) to implement some ML models (I have excluded the feature engineering steps)

1. EDA/Preprocessing Step

1.1 Perform the exploratory data analysis of different features, combination of features, see correlation between the features. With this step you will have a fell about the data and will get to know what features are actually important.

1.2 If you have sentences then consider removing the stopwords, perform stemming and remove any special characters.

1.3 Check for null values in the column. For numerical features you can impute them with mean imputation and similar techniques. For text features you can fill them as accordance with the problem. If you want, you can remove these null rows also.

1.4 If you are tackling the classification problem, then check the number of different class labels($Y_i$). If the dataset comes out to be imbalanced, then consider performing the upsampling/downsampling.

2. Data preparation step

2.1 Perform train-cv-test split

2.2 Perform the scaling of the numerical features. Use the mean and variance of the train dataset to scale the cv and test dataset

2.2 Convert the text into numerical vectors. For this you can use the word-embeddings like bag-of-words, tf-idf, word2vec etc. Learn the embedding/vocabulary from the train dataset only and then using these learned features transform your test ans cv datset

2.3 Let suppose you have $N$ datapoints and have columns as A(type = text), B(type = categorical) and C(type = numerical). Let us take the first row of the dataset. Let suppose after converting text into numerical vector and performing the one hot encoding, you get the first row as $[0.33\;1.978\;0.68]$ (value vector for column A), $[0\;1\;0]$(Value vector of column B) and $[9]$ (value vector for column C, this was already a numerical feature)
Now perform the concatenation step to get a single vector that you can feed the model. We will define vector $D$ as the concatenation of A, B and C. So $D\;=\;[0.33\;1.978\;0.68\;0\;1\;0\;9]$

2.4 Now you are ready to think about which model to implement

3.Training the model and prediction

3.1 Pick up a model, let say logistic regression/linear regression. Then Pick up a metric to check the performance of the model. For the above problem you are predicting the price so you may consider choosing Root mean square error(RMSE) as a metric.

3.2 Train the model and perform the hyperparameter tuning using the cross-validation score.

3.3 Use those hyperparameter and predict the results

You can google on how to implement the above steps. scikit-learn offers an excellent documentation, you may refer them.

Hope this helps!